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train · 840k rows

modelId string	author string	last_modified timestamp[us, tz=UTC]	downloads int64	likes int64	library_name string	tags sequence	pipeline_tag string	createdAt timestamp[us, tz=UTC]	card string
moaiz237/wav2vec2-base-timit-moaiz_exp2_new	moaiz237	2022-04-30T20:03:49Z	3	0	transformers	[ "transformers", "pytorch", "tensorboard", "wav2vec2", "automatic-speech-recognition", "generated_from_trainer", "license:apache-2.0", "endpoints_compatible", "region:us" ]	automatic-speech-recognition	2022-04-30T19:19:12Z	--- license: apache-2.0 tags: - generated_from_trainer model-index: - name: wav2vec2-base-timit-moaiz_exp2_new results: [] --- <!-- This model card has been generated automatically according to the information the Trainer had access to. You should probably proofread and complete it, then remove this comment. --> # wav2vec2-base-timit-moaiz_exp2_new This model is a fine-tuned version of [facebook/wav2vec2-base](https://huggingface.co/facebook/wav2vec2-base) on the None dataset. It achieves the following results on the evaluation set: - Loss: 0.6849 - Wer: 0.5396 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 0.0002 - train_batch_size: 16 - eval_batch_size: 8 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - lr_scheduler_warmup_steps: 1000 - num_epochs: 30 - mixed_precision_training: Native AMP ### Training results \| Training Loss \| Epoch \| Step \| Validation Loss \| Wer \| \|:-------------:\|:-----:\|:----:\|:---------------:\|:------:\| \| 4.1266 \| 13.89 \| 500 \| 1.0233 \| 0.7034 \| \| 0.5928 \| 27.78 \| 1000 \| 0.6849 \| 0.5396 \| ### Framework versions - Transformers 4.11.3 - Pytorch 1.11.0+cu113 - Datasets 1.18.3 - Tokenizers 0.10.3
julycodes/wav2vec2-base-timit-demo-colab-2	julycodes	2022-04-30T18:57:05Z	5	0	transformers	[ "transformers", "pytorch", "tensorboard", "wav2vec2", "automatic-speech-recognition", "generated_from_trainer", "license:apache-2.0", "endpoints_compatible", "region:us" ]	automatic-speech-recognition	2022-04-30T15:53:37Z	--- license: apache-2.0 tags: - generated_from_trainer model-index: - name: wav2vec2-base-timit-demo-colab-2 results: [] --- <!-- This model card has been generated automatically according to the information the Trainer had access to. You should probably proofread and complete it, then remove this comment. --> # wav2vec2-base-timit-demo-colab-2 This model is a fine-tuned version of [facebook/wav2vec2-base](https://huggingface.co/facebook/wav2vec2-base) on the None dataset. It achieves the following results on the evaluation set: - Loss: 0.7429 - Wer: 0.5080 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 0.0005 - train_batch_size: 10 - eval_batch_size: 8 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - lr_scheduler_warmup_steps: 900 - num_epochs: 30 - mixed_precision_training: Native AMP ### Training results \| Training Loss \| Epoch \| Step \| Validation Loss \| Wer \| \|:-------------:\|:-----:\|:----:\|:---------------:\|:------:\| \| 3.984 \| 8.77 \| 500 \| 0.9028 \| 0.7036 \| \| 0.6412 \| 17.54 \| 1000 \| 0.7275 \| 0.5868 \| \| 0.3073 \| 26.32 \| 1500 \| 0.7429 \| 0.5080 \| ### Framework versions - Transformers 4.11.3 - Pytorch 1.11.0+cu113 - Datasets 1.18.3 - Tokenizers 0.10.3
julycodes/wav2vec2-base-timit-demo-colab-3	julycodes	2022-04-30T18:32:37Z	3	0	transformers	[ "transformers", "pytorch", "tensorboard", "wav2vec2", "automatic-speech-recognition", "generated_from_trainer", "license:apache-2.0", "endpoints_compatible", "region:us" ]	automatic-speech-recognition	2022-04-30T16:19:14Z	--- license: apache-2.0 tags: - generated_from_trainer model-index: - name: wav2vec2-base-timit-demo-colab-3 results: [] --- <!-- This model card has been generated automatically according to the information the Trainer had access to. You should probably proofread and complete it, then remove this comment. --> # wav2vec2-base-timit-demo-colab-3 This model is a fine-tuned version of [facebook/wav2vec2-base](https://huggingface.co/facebook/wav2vec2-base) on the None dataset. It achieves the following results on the evaluation set: - Loss: 0.6622 - Wer: 0.5082 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 0.0001 - train_batch_size: 10 - eval_batch_size: 8 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - lr_scheduler_warmup_steps: 800 - num_epochs: 35 - mixed_precision_training: Native AMP ### Training results \| Training Loss \| Epoch \| Step \| Validation Loss \| Wer \| \|:-------------:\|:-----:\|:----:\|:---------------:\|:------:\| \| 2.2195 \| 8.77 \| 500 \| 0.9187 \| 0.6635 \| \| 0.5996 \| 17.54 \| 1000 \| 0.6569 \| 0.5347 \| \| 0.2855 \| 26.32 \| 1500 \| 0.6622 \| 0.5082 \| ### Framework versions - Transformers 4.11.3 - Pytorch 1.11.0+cu113 - Datasets 1.18.3 - Tokenizers 0.10.3
ParanoidAndroid/bert-finetuned-squad	ParanoidAndroid	2022-04-30T18:29:58Z	5	0	transformers	[ "transformers", "pytorch", "tensorboard", "bert", "question-answering", "generated_from_trainer", "license:apache-2.0", "endpoints_compatible", "region:us" ]	question-answering	2022-04-30T18:16:42Z	--- license: apache-2.0 tags: - generated_from_trainer model-index: - name: bert-finetuned-squad results: [] --- <!-- This model card has been generated automatically according to the information the Trainer had access to. You should probably proofread and complete it, then remove this comment. --> # bert-finetuned-squad This model is a fine-tuned version of [bert-base-cased](https://huggingface.co/bert-base-cased) on the None dataset. ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 2e-05 - train_batch_size: 8 - eval_batch_size: 8 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - num_epochs: 3 - mixed_precision_training: Native AMP ### Training results ### Framework versions - Transformers 4.18.0 - Pytorch 1.11.0+cu113 - Datasets 2.1.0 - Tokenizers 0.12.1
tahazakir/wav2vec2-base-timit-demo-colab0	tahazakir	2022-04-30T18:01:33Z	4	0	transformers	[ "transformers", "pytorch", "tensorboard", "wav2vec2", "automatic-speech-recognition", "generated_from_trainer", "license:apache-2.0", "endpoints_compatible", "region:us" ]	automatic-speech-recognition	2022-04-30T15:37:39Z	--- license: apache-2.0 tags: - generated_from_trainer model-index: - name: wav2vec2-base-timit-demo-colab0 results: [] --- <!-- This model card has been generated automatically according to the information the Trainer had access to. You should probably proofread and complete it, then remove this comment. --> # wav2vec2-base-timit-demo-colab0 This model is a fine-tuned version of [facebook/wav2vec2-base](https://huggingface.co/facebook/wav2vec2-base) on the None dataset. It achieves the following results on the evaluation set: - Loss: 0.8768 - Wer: 0.6089 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 0.0001 - train_batch_size: 16 - eval_batch_size: 8 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - lr_scheduler_warmup_steps: 1000 - num_epochs: 30 - mixed_precision_training: Native AMP ### Training results \| Training Loss \| Epoch \| Step \| Validation Loss \| Wer \| \|:-------------:\|:-----:\|:----:\|:---------------:\|:------:\| \| 5.1121 \| 13.89 \| 500 \| 2.9931 \| 1.0 \| \| 1.1475 \| 27.78 \| 1000 \| 0.8768 \| 0.6089 \| ### Framework versions - Transformers 4.11.3 - Pytorch 1.11.0+cu113 - Datasets 1.18.3 - Tokenizers 0.10.3
maxime7770/model	maxime7770	2022-04-30T15:12:40Z	5	0	transformers	[ "transformers", "tf", "camembert", "text-classification", "generated_from_keras_callback", "license:mit", "autotrain_compatible", "endpoints_compatible", "region:us" ]	text-classification	2022-04-29T11:54:14Z	--- license: mit tags: - generated_from_keras_callback model-index: - name: maxime7770/model results: [] --- <!-- This model card has been generated automatically according to the information Keras had access to. You should probably proofread and complete it, then remove this comment. --> # maxime7770/model This model is a fine-tuned version of [camembert-base](https://huggingface.co/camembert-base) on an unknown dataset. It achieves the following results on the evaluation set: - Train Loss: 0.1211 - Validation Loss: 0.4812 - Epoch: 49 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - optimizer: {'name': 'Adam', 'learning_rate': {'class_name': 'PolynomialDecay', 'config': {'initial_learning_rate': 2e-05, 'decay_steps': 650, 'end_learning_rate': 0.0, 'power': 1.0, 'cycle': False, 'name': None}}, 'decay': 0.0, 'beta_1': 0.9, 'beta_2': 0.999, 'epsilon': 1e-08, 'amsgrad': False} - training_precision: float32 ### Training results \| Train Loss \| Validation Loss \| Epoch \| \|:----------:\|:---------------:\|:-----:\| \| 1.5966 \| 1.5898 \| 0 \| \| 1.5577 \| 1.5576 \| 1 \| \| 1.5034 \| 1.4761 \| 2 \| \| 1.4034 \| 1.3538 \| 3 \| \| 1.2864 \| 1.2163 \| 4 \| \| 1.1502 \| 1.0980 \| 5 \| \| 1.0085 \| 0.9988 \| 6 \| \| 0.8828 \| 0.9130 \| 7 \| \| 0.7863 \| 0.8445 \| 8 \| \| 0.7036 \| 0.7871 \| 9 \| \| 0.6322 \| 0.7399 \| 10 \| \| 0.5731 \| 0.7030 \| 11 \| \| 0.5180 \| 0.6714 \| 12 \| \| 0.4757 \| 0.6432 \| 13 \| \| 0.4366 \| 0.6204 \| 14 \| \| 0.4057 \| 0.6006 \| 15 \| \| 0.3743 \| 0.5827 \| 16 \| \| 0.3475 \| 0.5689 \| 17 \| \| 0.3221 \| 0.5577 \| 18 \| \| 0.2971 \| 0.5467 \| 19 \| \| 0.2815 \| 0.5372 \| 20 \| \| 0.2700 \| 0.5297 \| 21 \| \| 0.2521 \| 0.5225 \| 22 \| \| 0.2343 \| 0.5168 \| 23 \| \| 0.2265 \| 0.5117 \| 24 \| \| 0.2143 \| 0.5074 \| 25 \| \| 0.2063 \| 0.5038 \| 26 \| \| 0.1941 \| 0.5001 \| 27 \| \| 0.1843 \| 0.4976 \| 28 \| \| 0.1782 \| 0.4949 \| 29 \| \| 0.2012 \| 0.4938 \| 30 \| \| 0.1691 \| 0.4930 \| 31 \| \| 0.1626 \| 0.4910 \| 32 \| \| 0.1884 \| 0.4886 \| 33 \| \| 0.1547 \| 0.4870 \| 34 \| \| 0.1492 \| 0.4858 \| 35 \| \| 0.1445 \| 0.4850 \| 36 \| \| 0.1415 \| 0.4842 \| 37 \| \| 0.1383 \| 0.4836 \| 38 \| \| 0.1374 \| 0.4832 \| 39 \| \| 0.1336 \| 0.4826 \| 40 \| \| 0.1322 \| 0.4823 \| 41 \| \| 0.1295 \| 0.4820 \| 42 \| \| 0.1268 \| 0.4818 \| 43 \| \| 0.1261 \| 0.4816 \| 44 \| \| 0.1253 \| 0.4815 \| 45 \| \| 0.1275 \| 0.4814 \| 46 \| \| 0.1247 \| 0.4812 \| 47 \| \| 0.1256 \| 0.4812 \| 48 \| \| 0.1211 \| 0.4812 \| 49 \| ### Framework versions - Transformers 4.18.0 - TensorFlow 2.8.0 - Datasets 2.1.0 - Tokenizers 0.12.1
ahmad573/wav2vec2-base-timit-demo-colab	ahmad573	2022-04-30T15:09:32Z	3	0	transformers	[ "transformers", "pytorch", "tensorboard", "wav2vec2", "automatic-speech-recognition", "generated_from_trainer", "license:apache-2.0", "endpoints_compatible", "region:us" ]	automatic-speech-recognition	2022-04-27T11:53:33Z	--- license: apache-2.0 tags: - generated_from_trainer model-index: - name: wav2vec2-base-timit-demo-colab results: [] --- <!-- This model card has been generated automatically according to the information the Trainer had access to. You should probably proofread and complete it, then remove this comment. --> # wav2vec2-base-timit-demo-colab This model is a fine-tuned version of [facebook/wav2vec2-base](https://huggingface.co/facebook/wav2vec2-base) on the None dataset. It achieves the following results on the evaluation set: - Loss: 0.5827 - Wer: 0.4147 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 0.0003 - train_batch_size: 8 - eval_batch_size: 8 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - lr_scheduler_warmup_steps: 1000 - num_epochs: 30 - mixed_precision_training: Native AMP ### Training results \| Training Loss \| Epoch \| Step \| Validation Loss \| Wer \| \|:-------------:\|:-----:\|:----:\|:---------------:\|:------:\| \| 0.4314 \| 7.04 \| 500 \| 0.5453 \| 0.4922 \| \| 0.2357 \| 14.08 \| 1000 \| 0.5573 \| 0.4376 \| \| 0.1283 \| 21.13 \| 1500 \| 0.5827 \| 0.4147 \| \| 0.1169 \| 28.17 \| 2000 \| 0.5827 \| 0.4147 \| ### Framework versions - Transformers 4.11.3 - Pytorch 1.11.0+cu113 - Datasets 1.18.3 - Tokenizers 0.10.3
Davincilee/door_inner	Davincilee	2022-04-30T15:07:38Z	0	1	null	[ "region:us" ]	null	2022-04-30T14:47:04Z	language: - "List of ISO 639-1 code for your language"
julycodes/wav2vec2-base-timit-demo-colab	julycodes	2022-04-30T14:40:55Z	5	0	transformers	[ "transformers", "pytorch", "tensorboard", "wav2vec2", "automatic-speech-recognition", "generated_from_trainer", "license:apache-2.0", "endpoints_compatible", "region:us" ]	automatic-speech-recognition	2022-04-30T11:54:03Z	--- license: apache-2.0 tags: - generated_from_trainer model-index: - name: wav2vec2-base-timit-demo-colab results: [] --- <!-- This model card has been generated automatically according to the information the Trainer had access to. You should probably proofread and complete it, then remove this comment. --> # wav2vec2-base-timit-demo-colab This model is a fine-tuned version of [facebook/wav2vec2-base](https://huggingface.co/facebook/wav2vec2-base) on the None dataset. It achieves the following results on the evaluation set: - Loss: 0.6574 - Wer: 0.5652 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 0.0001 - train_batch_size: 10 - eval_batch_size: 8 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - lr_scheduler_warmup_steps: 1000 - num_epochs: 20 - mixed_precision_training: Native AMP ### Training results \| Training Loss \| Epoch \| Step \| Validation Loss \| Wer \| \|:-------------:\|:-----:\|:----:\|:---------------:\|:------:\| \| 5.6258 \| 8.77 \| 500 \| 3.1693 \| 1.0 \| \| 1.4137 \| 17.54 \| 1000 \| 0.6574 \| 0.5652 \| ### Framework versions - Transformers 4.11.3 - Pytorch 1.11.0+cu113 - Datasets 1.18.3 - Tokenizers 0.10.3
Volodia/distilbert-base-uncased-finetuned-emotion	Volodia	2022-04-30T13:45:47Z	3	0	transformers	[ "transformers", "pytorch", "tensorboard", "distilbert", "text-classification", "generated_from_trainer", "dataset:emotion", "license:apache-2.0", "model-index", "autotrain_compatible", "endpoints_compatible", "region:us" ]	text-classification	2022-04-30T13:25:04Z	--- license: apache-2.0 tags: - generated_from_trainer datasets: - emotion metrics: - accuracy - f1 model-index: - name: distilbert-base-uncased-finetuned-emotion results: - task: name: Text Classification type: text-classification dataset: name: emotion type: emotion args: default metrics: - name: Accuracy type: accuracy value: 0.928 - name: F1 type: f1 value: 0.9280089473757943 --- <!-- This model card has been generated automatically according to the information the Trainer had access to. You should probably proofread and complete it, then remove this comment. --> # distilbert-base-uncased-finetuned-emotion This model is a fine-tuned version of [distilbert-base-uncased](https://huggingface.co/distilbert-base-uncased) on the emotion dataset. It achieves the following results on the evaluation set: - Loss: 0.2102 - Accuracy: 0.928 - F1: 0.9280 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 2e-05 - train_batch_size: 64 - eval_batch_size: 64 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - num_epochs: 2 ### Training results \| Training Loss \| Epoch \| Step \| Validation Loss \| Accuracy \| F1 \| \|:-------------:\|:-----:\|:----:\|:---------------:\|:--------:\|:------:\| \| 0.8028 \| 1.0 \| 250 \| 0.2998 \| 0.913 \| 0.9117 \| \| 0.2314 \| 2.0 \| 500 \| 0.2102 \| 0.928 \| 0.9280 \| ### Framework versions - Transformers 4.18.0 - Pytorch 1.11.0+cu113 - Datasets 2.1.0 - Tokenizers 0.12.1
learningdude/wav2vec2-base-finetuned-ks	learningdude	2022-04-30T13:35:56Z	3	0	transformers	[ "transformers", "pytorch", "tensorboard", "wav2vec2", "audio-classification", "generated_from_trainer", "dataset:superb", "license:apache-2.0", "endpoints_compatible", "region:us" ]	audio-classification	2022-04-30T07:56:06Z	--- license: apache-2.0 tags: - generated_from_trainer datasets: - superb metrics: - accuracy model-index: - name: wav2vec2-base-finetuned-ks results: [] --- <!-- This model card has been generated automatically according to the information the Trainer had access to. You should probably proofread and complete it, then remove this comment. --> # wav2vec2-base-finetuned-ks This model is a fine-tuned version of [facebook/wav2vec2-base](https://huggingface.co/facebook/wav2vec2-base) on the superb dataset. It achieves the following results on the evaluation set: - Loss: 0.0834 - Accuracy: 0.9840 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 3e-05 - train_batch_size: 32 - eval_batch_size: 32 - seed: 42 - gradient_accumulation_steps: 4 - total_train_batch_size: 128 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - lr_scheduler_warmup_ratio: 0.1 - num_epochs: 5 ### Training results \| Training Loss \| Epoch \| Step \| Validation Loss \| Accuracy \| \|:-------------:\|:-----:\|:----:\|:---------------:\|:--------:\| \| 0.6111 \| 1.0 \| 399 \| 0.5123 \| 0.9388 \| \| 0.2901 \| 2.0 \| 798 \| 0.1725 \| 0.9782 \| \| 0.1916 \| 3.0 \| 1197 \| 0.1060 \| 0.9834 \| \| 0.1754 \| 4.0 \| 1596 \| 0.0891 \| 0.9829 \| \| 0.1384 \| 5.0 \| 1995 \| 0.0834 \| 0.9840 \| ### Framework versions - Transformers 4.18.0 - Pytorch 1.11.0+cu113 - Datasets 1.14.0 - Tokenizers 0.12.1
sameearif88/wav2vec2-base-timit-demo-colab	sameearif88	2022-04-30T13:08:28Z	3	0	transformers	[ "transformers", "pytorch", "tensorboard", "wav2vec2", "automatic-speech-recognition", "generated_from_trainer", "license:apache-2.0", "endpoints_compatible", "region:us" ]	automatic-speech-recognition	2022-04-26T10:31:51Z	--- license: apache-2.0 tags: - generated_from_trainer model-index: - name: wav2vec2-base-timit-demo-colab results: [] --- <!-- This model card has been generated automatically according to the information the Trainer had access to. You should probably proofread and complete it, then remove this comment. --> # wav2vec2-base-timit-demo-colab This model is a fine-tuned version of [facebook/wav2vec2-base](https://huggingface.co/facebook/wav2vec2-base) on the None dataset. ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 0.0001 - train_batch_size: 16 - eval_batch_size: 8 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - lr_scheduler_warmup_steps: 1000 - num_epochs: 10 - mixed_precision_training: Native AMP ### Training results ### Framework versions - Transformers 4.11.3 - Pytorch 1.11.0+cu113 - Datasets 1.18.3 - Tokenizers 0.10.3
huggingtweets/itstomrobinson	huggingtweets	2022-04-30T07:06:15Z	3	0	transformers	[ "transformers", "pytorch", "gpt2", "text-generation", "huggingtweets", "en", "autotrain_compatible", "text-generation-inference", "endpoints_compatible", "region:us" ]	text-generation	2022-04-30T06:45:28Z	--- language: en thumbnail: http://www.huggingtweets.com/itstomrobinson/1651302371165/predictions.png tags: - huggingtweets widget: - text: "My dream is" --- <div class="inline-flex flex-col" style="line-height: 1.5;"> <div class="flex"> <div style="display:inherit; margin-left: 4px; margin-right: 4px; width: 92px; height:92px; border-radius: 50%; background-size: cover; background-image: url('https://pbs.twimg.com/profile_images/1388470365723168770/irz46Ykl_400x400.jpg')"> </div> <div style="display:none; margin-left: 4px; margin-right: 4px; width: 92px; height:92px; border-radius: 50%; background-size: cover; background-image: url('')"> </div> <div style="display:none; margin-left: 4px; margin-right: 4px; width: 92px; height:92px; border-radius: 50%; background-size: cover; background-image: url('')"> </div> </div> <div style="text-align: center; margin-top: 3px; font-size: 16px; font-weight: 800">🤖 AI BOT 🤖</div> <div style="text-align: center; font-size: 16px; font-weight: 800">Tom Robinson</div> <div style="text-align: center; font-size: 14px;">@itstomrobinson</div> </div> I was made with [huggingtweets](https://github.com/borisdayma/huggingtweets). Create your own bot based on your favorite user with [the demo](https://colab.research.google.com/github/borisdayma/huggingtweets/blob/master/huggingtweets-demo.ipynb)! ## How does it work? The model uses the following pipeline. ![pipeline](https://github.com/borisdayma/huggingtweets/blob/master/img/pipeline.png?raw=true) To understand how the model was developed, check the [W&B report](https://wandb.ai/wandb/huggingtweets/reports/HuggingTweets-Train-a-Model-to-Generate-Tweets--VmlldzoxMTY5MjI). ## Training data The model was trained on tweets from Tom Robinson. \| Data \| Tom Robinson \| \| --- \| --- \| \| Tweets downloaded \| 733 \| \| Retweets \| 40 \| \| Short tweets \| 52 \| \| Tweets kept \| 641 \| [Explore the data](https://wandb.ai/wandb/huggingtweets/runs/3bluc7sk/artifacts), which is tracked with [W&B artifacts](https://docs.wandb.com/artifacts) at every step of the pipeline. ## Training procedure The model is based on a pre-trained [GPT-2](https://huggingface.co/gpt2) which is fine-tuned on @itstomrobinson's tweets. Hyperparameters and metrics are recorded in the [W&B training run](https://wandb.ai/wandb/huggingtweets/runs/2ryc26oz) for full transparency and reproducibility. At the end of training, [the final model](https://wandb.ai/wandb/huggingtweets/runs/2ryc26oz/artifacts) is logged and versioned. ## How to use You can use this model directly with a pipeline for text generation: ```python from transformers import pipeline generator = pipeline('text-generation', model='huggingtweets/itstomrobinson') generator("My dream is", num_return_sequences=5) ``` ## Limitations and bias The model suffers from [the same limitations and bias as GPT-2](https://huggingface.co/gpt2#limitations-and-bias). In addition, the data present in the user's tweets further affects the text generated by the model. ## About Built by Boris Dayma [![Follow](https://img.shields.io/twitter/follow/borisdayma?style=social)](https://twitter.com/intent/follow?screen_name=borisdayma) For more details, visit the project repository. [![GitHub stars](https://img.shields.io/github/stars/borisdayma/huggingtweets?style=social)](https://github.com/borisdayma/huggingtweets)
obokkkk/mt5-base_2	obokkkk	2022-04-30T05:52:12Z	3	0	transformers	[ "transformers", "pytorch", "tensorboard", "mt5", "text2text-generation", "generated_from_trainer", "autotrain_compatible", "endpoints_compatible", "region:us" ]	text2text-generation	2022-04-29T06:50:46Z	--- tags: - generated_from_trainer metrics: - bleu model-index: - name: mt5-base_2 results: [] --- <!-- This model card has been generated automatically according to the information the Trainer had access to. You should probably proofread and complete it, then remove this comment. --> # mt5-base_2 This model is a fine-tuned version of [obokkkk/mt5-base](https://huggingface.co/obokkkk/mt5-base) on the None dataset. It achieves the following results on the evaluation set: - Loss: 1.1742 - Bleu: 9.479 - Gen Len: 16.9226 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 0.0001 - train_batch_size: 8 - eval_batch_size: 8 - seed: 42 - gradient_accumulation_steps: 256 - total_train_batch_size: 2048 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - num_epochs: 5 ### Training results \| Training Loss \| Epoch \| Step \| Validation Loss \| Bleu \| Gen Len \| \|:-------------:\|:-----:\|:----:\|:---------------:\|:------:\|:-------:\| \| No log \| 1.0 \| 183 \| 1.1834 \| 9.3761 \| 16.9129 \| \| No log \| 2.0 \| 366 \| 1.1791 \| 9.422 \| 16.9334 \| \| 1.3969 \| 3.0 \| 549 \| 1.1764 \| 9.4432 \| 16.9082 \| \| 1.3969 \| 4.0 \| 732 \| 1.1749 \| 9.461 \| 16.9157 \| \| 1.3969 \| 5.0 \| 915 \| 1.1742 \| 9.479 \| 16.9226 \| ### Framework versions - Transformers 4.18.0 - Pytorch 1.11.0+cu113 - Datasets 2.1.0 - Tokenizers 0.12.1
BigSalmon/CoverLetter	BigSalmon	2022-04-30T01:42:48Z	3	0	transformers	[ "transformers", "pytorch", "gpt2", "text-generation", "autotrain_compatible", "text-generation-inference", "endpoints_compatible", "region:us" ]	text-generation	2022-04-30T01:36:51Z	how to do initial prompt: captivated by [Enter Company Name]'s also trained on: https://huggingface.co/BigSalmon/InformalToFormalLincoln40 (so you can use those prompt outlines, too)
tonydiana1/distilroberta-base-finetuned-wikitext2	tonydiana1	2022-04-30T01:23:18Z	3	0	transformers	[ "transformers", "pytorch", "tensorboard", "roberta", "fill-mask", "generated_from_trainer", "license:apache-2.0", "autotrain_compatible", "endpoints_compatible", "region:us" ]	fill-mask	2022-04-30T01:01:59Z	--- license: apache-2.0 tags: - generated_from_trainer model-index: - name: distilroberta-base-finetuned-wikitext2 results: [] --- <!-- This model card has been generated automatically according to the information the Trainer had access to. You should probably proofread and complete it, then remove this comment. --> # distilroberta-base-finetuned-wikitext2 This model is a fine-tuned version of [distilroberta-base](https://huggingface.co/distilroberta-base) on the None dataset. It achieves the following results on the evaluation set: - Loss: 1.8347 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 2e-05 - train_batch_size: 8 - eval_batch_size: 8 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - num_epochs: 3.0 ### Training results \| Training Loss \| Epoch \| Step \| Validation Loss \| \|:-------------:\|:-----:\|:----:\|:---------------:\| \| 2.0853 \| 1.0 \| 2406 \| 1.9214 \| \| 1.986 \| 2.0 \| 4812 \| 1.8799 \| \| 1.9568 \| 3.0 \| 7218 \| 1.8202 \| ### Framework versions - Transformers 4.18.0 - Pytorch 1.11.0+cu113 - Datasets 2.1.0 - Tokenizers 0.12.1
Siddhart/t5-small-finetuned-xsum	Siddhart	2022-04-30T00:04:50Z	3	0	transformers	[ "transformers", "pytorch", "tensorboard", "t5", "text2text-generation", "generated_from_trainer", "license:apache-2.0", "autotrain_compatible", "text-generation-inference", "endpoints_compatible", "region:us" ]	text2text-generation	2022-04-29T23:51:32Z	--- license: apache-2.0 tags: - generated_from_trainer model-index: - name: t5-small-finetuned-xsum results: [] --- <!-- This model card has been generated automatically according to the information the Trainer had access to. You should probably proofread and complete it, then remove this comment. --> # t5-small-finetuned-xsum This model is a fine-tuned version of [t5-small](https://huggingface.co/t5-small) on an unknown dataset. ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 2e-05 - train_batch_size: 16 - eval_batch_size: 16 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - num_epochs: 1 - mixed_precision_training: Native AMP ### Training results \| Training Loss \| Epoch \| Step \| Validation Loss \| Rouge1 \| Rouge2 \| Rougel \| Rougelsum \| Gen Len \| \|:-------------:\|:-----:\|:----:\|:---------------:\|:-------:\|:-------:\|:-------:\|:---------:\|:-------:\| \| No log \| 1.0 \| 23 \| 2.7230 \| 33.2094 \| 14.0331 \| 28.4433 \| 29.4644 \| 18.8947 \| ### Framework versions - Transformers 4.18.0 - Pytorch 1.11.0+cu113 - Datasets 2.1.0 - Tokenizers 0.12.1
stas/tiny-m2m_100	stas	2022-04-29T23:57:25Z	1,370	0	transformers	[ "transformers", "pytorch", "m2m_100", "text2text-generation", "testing", "en", "license:apache-2.0", "autotrain_compatible", "endpoints_compatible", "region:us" ]	text2text-generation	2022-04-29T23:50:29Z	--- language: - en thumbnail: tags: - testing license: apache-2.0 --- # Tiny M2M100 model This is a tiny model that is used in the `transformers` test suite. It doesn't do anything useful beyond functional testing. Do not try to use it for anything that requires quality. The model is indeed 4MB in size. You can see how it was created [here](https://huggingface.co/stas/tiny-m2m_100/blob/main/m2m-make-tiny-model.py) If you're looking for the real model, please go to [https://huggingface.co/facebook/m2m100_418M](https://huggingface.co/facebook/m2m100_418M).
dhlanm/distilbert-base-uncased-finetune	dhlanm	2022-04-29T23:47:22Z	3	0	transformers	[ "transformers", "pytorch", "tensorboard", "distilbert", "token-classification", "generated_from_trainer", "license:apache-2.0", "autotrain_compatible", "endpoints_compatible", "region:us" ]	token-classification	2022-04-29T22:16:37Z	--- license: apache-2.0 tags: - generated_from_trainer metrics: - precision - recall - f1 - accuracy model-index: - name: distilbert-base-uncased-finetune results: [] --- <!-- This model card has been generated automatically according to the information the Trainer had access to. You should probably proofread and complete it, then remove this comment. --> # distilbert-base-uncased-finetune This model is a fine-tuned version of [distilbert-base-uncased](https://huggingface.co/distilbert-base-uncased) on the None dataset. It achieves the following results on the evaluation set: - Loss: 0.1315 - Precision: 0.0 - Recall: 0.0 - F1: 0.0 - Accuracy: 0.9715 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 2e-05 - train_batch_size: 8 - eval_batch_size: 8 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - num_epochs: 3 ### Training results \| Training Loss \| Epoch \| Step \| Validation Loss \| Precision \| Recall \| F1 \| Accuracy \| \|:-------------:\|:-----:\|:----:\|:---------------:\|:---------:\|:------:\|:---:\|:--------:\| \| No log \| 1.0 \| 48 \| 0.1349 \| 0.0 \| 0.0 \| 0.0 \| 0.9715 \| \| No log \| 2.0 \| 96 \| 0.1318 \| 0.0 \| 0.0 \| 0.0 \| 0.9715 \| \| No log \| 3.0 \| 144 \| 0.1315 \| 0.0 \| 0.0 \| 0.0 \| 0.9715 \| ### Framework versions - Transformers 4.18.0 - Pytorch 1.11.0+cu113 - Datasets 2.1.0 - Tokenizers 0.12.1
doc2query/msmarco-vietnamese-mt5-base-v1	doc2query	2022-04-29T22:06:03Z	18	4	transformers	[ "transformers", "pytorch", "mt5", "text2text-generation", "vi", "dataset:unicamp-dl/mmarco", "arxiv:1904.08375", "arxiv:2104.08663", "arxiv:2112.07577", "license:apache-2.0", "autotrain_compatible", "endpoints_compatible", "region:us" ]	text2text-generation	2022-04-29T22:05:47Z	--- language: vi datasets: - unicamp-dl/mmarco widget: - text: "Python (phát âm tiếng Anh: /ˈpaɪθɑːn/) là một ngôn ngữ lập trình bậc cao cho các mục đích lập trình đa năng, do Guido van Rossum tạo ra và lần đầu ra mắt vào năm 1991. Python được thiết kế với ưu điểm mạnh là dễ đọc, dễ học và dễ nhớ. Python là ngôn ngữ có hình thức rất sáng sủa, cấu trúc rõ ràng, thuận tiện cho người mới học lập trình và là ngôn ngữ lập trình dễ học; được dùng rộng rãi trong phát triển trí tuệ nhân tạo. Cấu trúc của Python còn cho phép người sử dụng viết mã lệnh với số lần gõ phím tối thiểu. Vào tháng 7 năm 2018, van Rossum đã từ chức lãnh đạo trong cộng đồng ngôn ngữ Python sau 30 năm làm việc." license: apache-2.0 --- # doc2query/msmarco-vietnamese-mt5-base-v1 This is a [doc2query](https://arxiv.org/abs/1904.08375) model based on mT5 (also known as [docT5query](https://cs.uwaterloo.ca/~jimmylin/publications/Nogueira_Lin_2019_docTTTTTquery-v2.pdf)). It can be used for: - Document expansion: You generate for your paragraphs 20-40 queries and index the paragraphs and the generates queries in a standard BM25 index like Elasticsearch, OpenSearch, or Lucene. The generated queries help to close the lexical gap of lexical search, as the generate queries contain synonyms. Further, it re-weights words giving important words a higher weight even if they appear seldomn in a paragraph. In our [BEIR](https://arxiv.org/abs/2104.08663) paper we showed that BM25+docT5query is a powerful search engine. In the [BEIR repository](https://github.com/beir-cellar/beir) we have an example how to use docT5query with Pyserini. - Domain Specific Training Data Generation: It can be used to generate training data to learn an embedding model. In our [GPL-Paper](https://arxiv.org/abs/2112.07577) / [GPL Example on SBERT.net](https://www.sbert.net/examples/domain_adaptation/README.html#gpl-generative-pseudo-labeling) we have an example how to use the model to generate (query, text) pairs for a given collection of unlabeled texts. These pairs can then be used to train powerful dense embedding models. ## Usage ```python from transformers import AutoTokenizer, AutoModelForSeq2SeqLM import torch model_name = 'doc2query/msmarco-vietnamese-mt5-base-v1' tokenizer = AutoTokenizer.from_pretrained(model_name) model = AutoModelForSeq2SeqLM.from_pretrained(model_name) text = "Python (phát âm tiếng Anh: /ˈpaɪθɑːn/) là một ngôn ngữ lập trình bậc cao cho các mục đích lập trình đa năng, do Guido van Rossum tạo ra và lần đầu ra mắt vào năm 1991. Python được thiết kế với ưu điểm mạnh là dễ đọc, dễ học và dễ nhớ. Python là ngôn ngữ có hình thức rất sáng sủa, cấu trúc rõ ràng, thuận tiện cho người mới học lập trình và là ngôn ngữ lập trình dễ học; được dùng rộng rãi trong phát triển trí tuệ nhân tạo. Cấu trúc của Python còn cho phép người sử dụng viết mã lệnh với số lần gõ phím tối thiểu. Vào tháng 7 năm 2018, van Rossum đã từ chức lãnh đạo trong cộng đồng ngôn ngữ Python sau 30 năm làm việc." def create_queries(para): input_ids = tokenizer.encode(para, return_tensors='pt') with torch.no_grad(): # Here we use top_k / top_k random sampling. It generates more diverse queries, but of lower quality sampling_outputs = model.generate( input_ids=input_ids, max_length=64, do_sample=True, top_p=0.95, top_k=10, num_return_sequences=5 ) # Here we use Beam-search. It generates better quality queries, but with less diversity beam_outputs = model.generate( input_ids=input_ids, max_length=64, num_beams=5, no_repeat_ngram_size=2, num_return_sequences=5, early_stopping=True ) print("Paragraph:") print(para) print("\nBeam Outputs:") for i in range(len(beam_outputs)): query = tokenizer.decode(beam_outputs[i], skip_special_tokens=True) print(f'{i + 1}: {query}') print("\nSampling Outputs:") for i in range(len(sampling_outputs)): query = tokenizer.decode(sampling_outputs[i], skip_special_tokens=True) print(f'{i + 1}: {query}') create_queries(text) ``` Note: `model.generate()` is non-deterministic for top_k/top_n sampling. It produces different queries each time you run it. ## Training This model fine-tuned [google/mt5-base](https://huggingface.co/google/mt5-base) for 66k training steps (4 epochs on the 500k training pairs from MS MARCO). For the training script, see the `train_script.py` in this repository. The input-text was truncated to 320 word pieces. Output text was generated up to 64 word pieces. This model was trained on a (query, passage) from the [mMARCO dataset](https://github.com/unicamp-dl/mMARCO).
espnet/turkish_commonvoice_blstm	espnet	2022-04-29T21:33:48Z	0	0	espnet	[ "espnet", "audio", "automatic-speech-recognition", "tr", "dataset:commonvoice", "arxiv:1804.00015", "license:cc-by-4.0", "region:us" ]	automatic-speech-recognition	2022-04-29T21:32:59Z	--- tags: - espnet - audio - automatic-speech-recognition language: tr datasets: - commonvoice license: cc-by-4.0 --- ## ESPnet2 ASR model ### `espnet/turkish_commonvoice_blstm` This model was trained by dzeinali using commonvoice recipe in [espnet](https://github.com/espnet/espnet/). ### Demo: How to use in ESPnet2 ```bash cd espnet git checkout 716eb8f92e19708acfd08ba3bd39d40890d3a84b pip install -e . cd egs2/commonvoice/asr1 ./run.sh --skip_data_prep false --skip_train true --download_model espnet/turkish_commonvoice_blstm ``` <!-- Generated by scripts/utils/show_asr_result.sh --> # RESULTS ## Environments - date: `Sat Apr 16 17:16:06 EDT 2022` - python version: `3.9.5 (default, Jun 4 2021, 12:28:51) [GCC 7.5.0]` - espnet version: `espnet 0.10.6a1` - pytorch version: `pytorch 1.8.1+cu102` - Git hash: `5e6e95d087af8a7a4c33c4248b75114237eae64b` - Commit date: `Mon Apr 4 21:04:45 2022 -0400` ## asr_tr_50_epoch_lr_0.1 ### WER \|dataset\|Snt\|Wrd\|Corr\|Sub\|Del\|Ins\|Err\|S.Err\| \|---\|---\|---\|---\|---\|---\|---\|---\|---\| \|decode_rnn_asr_model_valid.acc.ave/test_tr\|8339\|43647\|78.5\|19.6\|2.0\|1.6\|23.1\|50.9\| ### CER \|dataset\|Snt\|Wrd\|Corr\|Sub\|Del\|Ins\|Err\|S.Err\| \|---\|---\|---\|---\|---\|---\|---\|---\|---\| \|decode_rnn_asr_model_valid.acc.ave/test_tr\|8339\|306849\|94.3\|3.2\|2.5\|1.1\|6.8\|50.9\| ### TER \|dataset\|Snt\|Wrd\|Corr\|Sub\|Del\|Ins\|Err\|S.Err\| \|---\|---\|---\|---\|---\|---\|---\|---\|---\| \|decode_rnn_asr_model_valid.acc.ave/test_tr\|8339\|203431\|91.0\|5.8\|3.2\|1.3\|10.3\|50.6\| ## ASR config <details><summary>expand</summary> ``` config: conf/tuning/train_asr_rnn_tr.yaml print_config: false log_level: INFO dry_run: false iterator_type: sequence output_dir: exp/asr_tr_50_epoch_lr_0.1 ngpu: 1 seed: 0 num_workers: 1 num_att_plot: 3 dist_backend: nccl dist_init_method: env:// dist_world_size: null dist_rank: null local_rank: 0 dist_master_addr: null dist_master_port: null dist_launcher: null multiprocessing_distributed: false unused_parameters: false sharded_ddp: false cudnn_enabled: true cudnn_benchmark: false cudnn_deterministic: true collect_stats: false write_collected_feats: false max_epoch: 50 patience: 3 val_scheduler_criterion: - valid - loss early_stopping_criterion: - valid - loss - min best_model_criterion: - - train - loss - min - - valid - loss - min - - train - acc - max - - valid - acc - max keep_nbest_models: - 10 nbest_averaging_interval: 0 grad_clip: 5.0 grad_clip_type: 2.0 grad_noise: false accum_grad: 1 no_forward_run: false resume: true train_dtype: float32 use_amp: false log_interval: null use_matplotlib: true use_tensorboard: true use_wandb: false wandb_project: null wandb_id: null wandb_entity: null wandb_name: null wandb_model_log_interval: -1 detect_anomaly: false pretrain_path: null init_param: [] ignore_init_mismatch: false freeze_param: [] num_iters_per_epoch: null batch_size: 16 valid_batch_size: null batch_bins: 1000000 valid_batch_bins: null train_shape_file: - exp/asr_stats_raw_tr_bpe150_sp/train/speech_shape - exp/asr_stats_raw_tr_bpe150_sp/train/text_shape.bpe valid_shape_file: - exp/asr_stats_raw_tr_bpe150_sp/valid/speech_shape - exp/asr_stats_raw_tr_bpe150_sp/valid/text_shape.bpe batch_type: folded valid_batch_type: null fold_length: - 80000 - 150 sort_in_batch: descending sort_batch: descending multiple_iterator: false chunk_length: 500 chunk_shift_ratio: 0.5 num_cache_chunks: 1024 train_data_path_and_name_and_type: - - dump/raw/train_tr_sp/wav.scp - speech - sound - - dump/raw/train_tr_sp/text - text - text valid_data_path_and_name_and_type: - - dump/raw/dev_tr/wav.scp - speech - sound - - dump/raw/dev_tr/text - text - text allow_variable_data_keys: false max_cache_size: 0.0 max_cache_fd: 32 valid_max_cache_size: null optim: adadelta optim_conf: lr: 0.1 scheduler: null scheduler_conf: {} token_list: - <blank> - <unk> - ▁ - R - K - E - . - I - N - L - ı - A - M - T - U - Y - S - Z - ş - ü - O - ▁A - ç - DI - MA - IN - ▁BU - LA - ',' - H - RA - LAR - ▁BIR - DE - ME - ö - '?' - Dı - DA - AN - ▁KA - LI - LER - F - LE - EN - P - B - V - DU - YE - UN - ▁G - TE - ▁BE - BI - YA - KI - Tı - BA - ▁OL - TI - ▁DE - ▁HA - ▁YA - ıN - AR - IM - Sı - D - Lı - ER - C - ▁S - NA - üN - IYOR - ▁NE - ▁I - ▁O - ▁SA - ▁" - ▁DA - SI - G - ▁P - TA - ▁SE - ▁VE - KA - '''' - UM - DEN - ▁GE - Dü - ." - ıYOR - ▁TA - '!' - CE - VA - ▁HE - UZ - GI - ıNDA - ıNı - ▁MI - LAN - ▁BAş - ▁ON - CA - İ - DAN - SIN - '...' - ▁DO - ▁GöR - ▁KO - ▁VAR - ACAK - ▁GEL - ▁YAP - ▁SON - ▁ET - ▁IKI - Ç - Ş - '"' - J - Ö - ':' - â - Ü - ; - '-' - W - X - ’ - ” - ‘ - î - ë - Q - ( - Â - û - “ - ) - ğ - <sos/eos> init: null input_size: null ctc_conf: dropout_rate: 0.0 ctc_type: builtin reduce: true ignore_nan_grad: true joint_net_conf: null model_conf: ctc_weight: 0.5 use_preprocessor: true token_type: bpe bpemodel: data/tr_token_list/bpe_unigram150/bpe.model non_linguistic_symbols: null cleaner: null g2p: null speech_volume_normalize: null rir_scp: null rir_apply_prob: 1.0 noise_scp: null noise_apply_prob: 1.0 noise_db_range: '13_15' frontend: default frontend_conf: fs: 16k specaug: specaug specaug_conf: apply_time_warp: true time_warp_window: 5 time_warp_mode: bicubic apply_freq_mask: true freq_mask_width_range: - 0 - 27 num_freq_mask: 2 apply_time_mask: true time_mask_width_ratio_range: - 0.0 - 0.05 num_time_mask: 2 normalize: global_mvn normalize_conf: stats_file: exp/asr_stats_raw_tr_bpe150_sp/train/feats_stats.npz preencoder: null preencoder_conf: {} encoder: vgg_rnn encoder_conf: rnn_type: lstm bidirectional: true use_projection: true num_layers: 4 hidden_size: 1024 output_size: 1024 postencoder: null postencoder_conf: {} decoder: rnn decoder_conf: num_layers: 2 hidden_size: 1024 sampling_probability: 0 att_conf: atype: location adim: 1024 aconv_chans: 10 aconv_filts: 100 required: - output_dir - token_list version: 0.10.6a1 distributed: false ``` </details> ### Citing ESPnet ```BibTex @inproceedings{watanabe2018espnet, author={Shinji Watanabe and Takaaki Hori and Shigeki Karita and Tomoki Hayashi and Jiro Nishitoba and Yuya Unno and Nelson Yalta and Jahn Heymann and Matthew Wiesner and Nanxin Chen and Adithya Renduchintala and Tsubasa Ochiai}, title={{ESPnet}: End-to-End Speech Processing Toolkit}, year={2018}, booktitle={Proceedings of Interspeech}, pages={2207--2211}, doi={10.21437/Interspeech.2018-1456}, url={http://dx.doi.org/10.21437/Interspeech.2018-1456} } ``` or arXiv: ```bibtex @misc{watanabe2018espnet, title={ESPnet: End-to-End Speech Processing Toolkit}, author={Shinji Watanabe and Takaaki Hori and Shigeki Karita and Tomoki Hayashi and Jiro Nishitoba and Yuya Unno and Nelson Yalta and Jahn Heymann and Matthew Wiesner and Nanxin Chen and Adithya Renduchintala and Tsubasa Ochiai}, year={2018}, eprint={1804.00015}, archivePrefix={arXiv}, primaryClass={cs.CL} } ```
espnet/french_commonvoice_blstm	espnet	2022-04-29T21:22:54Z	0	0	espnet	[ "espnet", "audio", "automatic-speech-recognition", "fr", "dataset:commonvoice", "arxiv:1804.00015", "license:cc-by-4.0", "region:us" ]	automatic-speech-recognition	2022-04-29T21:22:08Z	--- tags: - espnet - audio - automatic-speech-recognition language: fr datasets: - commonvoice license: cc-by-4.0 --- ## ESPnet2 ASR model ### `espnet/french_commonvoice_blstm` This model was trained by dzeinali using commonvoice recipe in [espnet](https://github.com/espnet/espnet/). ### Demo: How to use in ESPnet2 ```bash cd espnet git checkout 716eb8f92e19708acfd08ba3bd39d40890d3a84b pip install -e . cd egs2/commonvoice/asr1 ./run.sh --skip_data_prep false --skip_train true --download_model espnet/french_commonvoice_blstm ``` <!-- Generated by scripts/utils/show_asr_result.sh --> # RESULTS ## Environments - date: `Fri Apr 29 17:20:37 EDT 2022` - python version: `3.9.5 (default, Jun 4 2021, 12:28:51) [GCC 7.5.0]` - espnet version: `espnet 0.10.6a1` - pytorch version: `pytorch 1.8.1+cu102` - Git hash: `716eb8f92e19708acfd08ba3bd39d40890d3a84b` - Commit date: `Thu Apr 28 19:50:59 2022 -0400` ## asr_train_asr_rnn_raw_fr_bpe350_sp ### WER \|dataset\|Snt\|Wrd\|Corr\|Sub\|Del\|Ins\|Err\|S.Err\| \|---\|---\|---\|---\|---\|---\|---\|---\|---\| \|decode_rnn_asr_model_valid.acc.best/test_fr\|15621\|151227\|75.1\|22.6\|2.3\|2.3\|27.2\|81.0\| ### CER \|dataset\|Snt\|Wrd\|Corr\|Sub\|Del\|Ins\|Err\|S.Err\| \|---\|---\|---\|---\|---\|---\|---\|---\|---\| \|decode_rnn_asr_model_valid.acc.best/test_fr\|15621\|952803\|92.9\|3.6\|3.5\|2.0\|9.1\|81.0\| ### TER \|dataset\|Snt\|Wrd\|Corr\|Sub\|Del\|Ins\|Err\|S.Err\| \|---\|---\|---\|---\|---\|---\|---\|---\|---\| \|decode_rnn_asr_model_valid.acc.best/test_fr\|15621\|730898\|89.9\|6.5\|3.6\|1.9\|12.0\|81.0\| ## ASR config <details><summary>expand</summary> ``` config: conf/tuning/train_asr_rnn.yaml print_config: false log_level: INFO dry_run: false iterator_type: sequence output_dir: exp/asr_train_asr_rnn_raw_fr_bpe350_sp ngpu: 1 seed: 0 num_workers: 1 num_att_plot: 3 dist_backend: nccl dist_init_method: env:// dist_world_size: null dist_rank: null local_rank: 0 dist_master_addr: null dist_master_port: null dist_launcher: null multiprocessing_distributed: false unused_parameters: false sharded_ddp: false cudnn_enabled: true cudnn_benchmark: false cudnn_deterministic: true collect_stats: false write_collected_feats: false max_epoch: 15 patience: 3 val_scheduler_criterion: - valid - loss early_stopping_criterion: - valid - loss - min best_model_criterion: - - train - loss - min - - valid - loss - min - - train - acc - max - - valid - acc - max keep_nbest_models: - 10 nbest_averaging_interval: 0 grad_clip: 5.0 grad_clip_type: 2.0 grad_noise: false accum_grad: 1 no_forward_run: false resume: true train_dtype: float32 use_amp: false log_interval: null use_matplotlib: true use_tensorboard: true use_wandb: false wandb_project: null wandb_id: null wandb_entity: null wandb_name: null wandb_model_log_interval: -1 detect_anomaly: false pretrain_path: null init_param: [] ignore_init_mismatch: false freeze_param: [] num_iters_per_epoch: null batch_size: 30 valid_batch_size: null batch_bins: 1000000 valid_batch_bins: null train_shape_file: - exp/asr_stats_raw_fr_bpe350_sp/train/speech_shape - exp/asr_stats_raw_fr_bpe350_sp/train/text_shape.bpe valid_shape_file: - exp/asr_stats_raw_fr_bpe350_sp/valid/speech_shape - exp/asr_stats_raw_fr_bpe350_sp/valid/text_shape.bpe batch_type: folded valid_batch_type: null fold_length: - 80000 - 150 sort_in_batch: descending sort_batch: descending multiple_iterator: false chunk_length: 500 chunk_shift_ratio: 0.5 num_cache_chunks: 1024 train_data_path_and_name_and_type: - - dump/raw/train_fr_sp/wav.scp - speech - sound - - dump/raw/train_fr_sp/text - text - text valid_data_path_and_name_and_type: - - dump/raw/dev_fr/wav.scp - speech - sound - - dump/raw/dev_fr/text - text - text allow_variable_data_keys: false max_cache_size: 0.0 max_cache_fd: 32 valid_max_cache_size: null optim: adadelta optim_conf: lr: 0.1 scheduler: null scheduler_conf: {} token_list: - <blank> - <unk> - S - ▁ - E - I - T - A - U - O - . - L - R - é - P - C - V - 'ON' - M - ▁DE - ',' - N - ▁S - D - IN - '''' - OU - ▁D - G - IS - ▁P - ER - ▁C - ▁L - ▁LA - B - ▁" - ▁A - RE - AN - ." - ▁M - ▁F - '-' - F - ▁T - ES - ENT - ▁LE - EN - IT - LE - ▁N - è - H - ’ - Y - X - Z - K - J - ê - '?' - '!' - É - ç - W - à - ô - â - Q - î - À - '"' - œ - û - ù - ï - ':' - ; - — - È - « - » - Ç - Ê - ë - á - ü - í - ö - ó - ) - Î - Â - ō - ä - – - Ô - ć - š - '&' - ñ - '=' - ł - č - Û - ú - ū - ø - ā - ã - ă - / - ń - _ - ș - å - æ - ° - ß - “ - ” - ž - ı - Œ - Ö - ř - Š - ý - Ō - ‘ - ş - · - o - ę - ÿ - Å - ą - ð - ī - ò - ż - ě - ś - '`' - Ë - ì - ē - ğ - İ - '*' - Í - ė - Ó - ő - đ - ʻ - Ü - õ - Ä - ņ - ṣ - '\|' - ʾ - π - Ā - σ - '%' - ả - κ - ʼ - ň - Ú - ļ - ư - '1' - '2' - '}' - ĩ - Ҫ - ا - ầ - ⁄ - ṇ - þ - ǎ - ο - ′ - s - § - ľ - ǹ - Ʉ - ː - ̱ - γ - ν - ن - ạ - ễ - ộ - ≥ - 星 - ề - ṯ - τ - δ - Δ - Ț - Ș - Ū - Ř - ∆ - → - ệ - Г - ơ - ţ - Þ - Ñ - ± - ť - ŏ - € - „ - ʿ - Ć - £ - α - Ż - Ş - β - ź - Đ - Ø - Ś - Ž - Æ - $ - Ï - Ł - ț - Č - Á - ́ - Ù - Μ - ι - ρ - ό - И - з - 京 - 北 - ď - Ġ - Ṭ - − - ☉ - '~' - ® - Ì - Ò - Õ - × - ħ - ĺ - Ľ - ũ - ů - Ų - ǃ - ǔ - ̠ - ̲ - Κ - Π - ε - ζ - μ - ς - υ - ψ - І - Ј - А - Е - П - а - е - м - н - Գ - Զ - ب - د - ر - ل - و - ي - ወ - ደ - ḍ - ṅ - ṭ - ậ - ắ - ẵ - ị - ồ - ờ - ợ - ủ - ‐ - ― - † - ‹ - › - ₽ - ∈ - ∞ - ─ - い - う - た - つ - へ - ま - め - や - ゔ - 扬 - 术 - 美 - 貴 - 青 - 馆 - Ꝑ - ̐ - Ω - ử - ỳ - ∨ - 乃 - 杜 - ( - Ē - ǫ - <sos/eos> init: null input_size: null ctc_conf: dropout_rate: 0.0 ctc_type: builtin reduce: true ignore_nan_grad: true joint_net_conf: null model_conf: ctc_weight: 0.5 use_preprocessor: true token_type: bpe bpemodel: data/fr_token_list/bpe_unigram350/bpe.model non_linguistic_symbols: null cleaner: null g2p: null speech_volume_normalize: null rir_scp: null rir_apply_prob: 1.0 noise_scp: null noise_apply_prob: 1.0 noise_db_range: '13_15' frontend: default frontend_conf: fs: 16k specaug: specaug specaug_conf: apply_time_warp: true time_warp_window: 5 time_warp_mode: bicubic apply_freq_mask: true freq_mask_width_range: - 0 - 27 num_freq_mask: 2 apply_time_mask: true time_mask_width_ratio_range: - 0.0 - 0.05 num_time_mask: 2 normalize: global_mvn normalize_conf: stats_file: exp/asr_stats_raw_fr_bpe350_sp/train/feats_stats.npz preencoder: null preencoder_conf: {} encoder: vgg_rnn encoder_conf: rnn_type: lstm bidirectional: true use_projection: true num_layers: 4 hidden_size: 1024 output_size: 1024 postencoder: null postencoder_conf: {} decoder: rnn decoder_conf: num_layers: 2 hidden_size: 1024 sampling_probability: 0 att_conf: atype: location adim: 1024 aconv_chans: 10 aconv_filts: 100 required: - output_dir - token_list version: 0.10.6a1 distributed: false ``` </details> ### Citing ESPnet ```BibTex @inproceedings{watanabe2018espnet, author={Shinji Watanabe and Takaaki Hori and Shigeki Karita and Tomoki Hayashi and Jiro Nishitoba and Yuya Unno and Nelson Yalta and Jahn Heymann and Matthew Wiesner and Nanxin Chen and Adithya Renduchintala and Tsubasa Ochiai}, title={{ESPnet}: End-to-End Speech Processing Toolkit}, year={2018}, booktitle={Proceedings of Interspeech}, pages={2207--2211}, doi={10.21437/Interspeech.2018-1456}, url={http://dx.doi.org/10.21437/Interspeech.2018-1456} } ``` or arXiv: ```bibtex @misc{watanabe2018espnet, title={ESPnet: End-to-End Speech Processing Toolkit}, author={Shinji Watanabe and Takaaki Hori and Shigeki Karita and Tomoki Hayashi and Jiro Nishitoba and Yuya Unno and Nelson Yalta and Jahn Heymann and Matthew Wiesner and Nanxin Chen and Adithya Renduchintala and Tsubasa Ochiai}, year={2018}, eprint={1804.00015}, archivePrefix={arXiv}, primaryClass={cs.CL} } ```
timhbach/Team_Gryffindor_NER	timhbach	2022-04-29T21:13:30Z	4	0	transformers	[ "transformers", "pytorch", "distilbert", "token-classification", "generated_from_trainer", "license:apache-2.0", "autotrain_compatible", "endpoints_compatible", "region:us" ]	token-classification	2022-04-11T07:08:50Z	--- license: apache-2.0 tags: - generated_from_trainer metrics: - precision - recall - f1 - accuracy model-index: - name: Team_Gryffindor_NER results: [] --- <!-- This model card has been generated automatically according to the information the Trainer had access to. You should probably proofread and complete it, then remove this comment. --> # Team-Gryffindor-distilbert-base-finetuned-NER-creditcardcontract-100epoch This model is a fine-tuned version of [distilbert-base-uncased](https://huggingface.co/distilbert-base-uncased) on the Credit card agreement dataset. It achieves the following results on the evaluation set: - Loss: 0.0470 - Precision: 0.7319 - Recall: 0.7064 - F1: 0.7190 - Accuracy: 0.9920 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 2e-05 - train_batch_size: 16 - eval_batch_size: 16 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - num_epochs: 11 ### Training results \| Training Loss \| Epoch \| Step \| Validation Loss \| Precision \| Recall \| F1 \| Accuracy \| \|:-------------:\|:-----:\|:------:\|:---------------:\|:---------:\|:------:\|:------:\|:--------:\| \| 0.0113 \| 0.33 \| 500 \| 0.0443 \| 0.6547 \| 0.7028 \| 0.6779 \| 0.9908 \| \| 0.0118 \| 0.67 \| 1000 \| 0.0435 \| 0.7207 \| 0.6440 \| 0.6802 \| 0.9916 \| \| 0.013 \| 1.0 \| 1500 \| 0.0449 \| 0.7113 \| 0.6826 \| 0.6966 \| 0.9918 \| \| 0.0113 \| 1.34 \| 2000 \| 0.0434 \| 0.7213 \| 0.6697 \| 0.6946 \| 0.9915 \| \| 0.0121 \| 1.67 \| 2500 \| 0.0467 \| 0.6955 \| 0.6789 \| 0.6871 \| 0.9914 \| \| 0.0125 \| 2.01 \| 3000 \| 0.0417 \| 0.7095 \| 0.6991 \| 0.7043 \| 0.9920 \| \| 0.0106 \| 2.34 \| 3500 \| 0.0437 \| 0.7191 \| 0.6624 \| 0.6896 \| 0.9918 \| \| 0.0114 \| 2.68 \| 4000 \| 0.0468 \| 0.7165 \| 0.6679 \| 0.6914 \| 0.9920 \| \| 0.0125 \| 3.01 \| 4500 \| 0.0431 \| 0.6888 \| 0.6862 \| 0.6875 \| 0.9917 \| \| 0.0107 \| 3.35 \| 5000 \| 0.0446 \| 0.7184 \| 0.6459 \| 0.6802 \| 0.9913 \| \| 0.0096 \| 3.68 \| 5500 \| 0.0485 \| 0.6926 \| 0.6532 \| 0.6723 \| 0.9912 \| \| 0.013 \| 4.02 \| 6000 \| 0.0448 \| 0.6134 \| 0.6697 \| 0.6404 \| 0.9907 \| \| 0.0102 \| 4.35 \| 6500 \| 0.0497 \| 0.6895 \| 0.6642 \| 0.6766 \| 0.9913 \| \| 0.0112 \| 4.69 \| 7000 \| 0.0464 \| 0.6759 \| 0.6697 \| 0.6728 \| 0.9910 \| \| 0.0117 \| 5.02 \| 7500 \| 0.0484 \| 0.7451 \| 0.6275 \| 0.6813 \| 0.9916 \| \| 0.0114 \| 5.36 \| 8000 \| 0.0411 \| 0.7086 \| 0.6826 \| 0.6953 \| 0.9919 \| \| 0.0108 \| 5.69 \| 8500 \| 0.0443 \| 0.7041 \| 0.6679 \| 0.6855 \| 0.9916 \| \| 0.0109 \| 6.03 \| 9000 \| 0.0470 \| 0.7228 \| 0.6697 \| 0.6952 \| 0.9916 \| \| 0.0099 \| 6.36 \| 9500 \| 0.0471 \| 0.7253 \| 0.6881 \| 0.7062 \| 0.9913 \| \| 0.0103 \| 6.7 \| 10000 \| 0.0430 \| 0.6986 \| 0.7101 \| 0.7043 \| 0.9914 \| \| 0.0117 \| 7.03 \| 10500 \| 0.0462 \| 0.7327 \| 0.6991 \| 0.7155 \| 0.9918 \| \| 0.0098 \| 7.37 \| 11000 \| 0.0483 \| 0.6910 \| 0.6771 \| 0.6840 \| 0.9914 \| \| 0.0107 \| 7.7 \| 11500 \| 0.0468 \| 0.7189 \| 0.6899 \| 0.7041 \| 0.9916 \| \| 0.0119 \| 8.04 \| 12000 \| 0.0434 \| 0.6970 \| 0.6881 \| 0.6925 \| 0.9918 \| \| 0.0112 \| 8.37 \| 12500 \| 0.0469 \| 0.7007 \| 0.6917 \| 0.6962 \| 0.9918 \| \| 0.011 \| 8.71 \| 13000 \| 0.0469 \| 0.6736 \| 0.6514 \| 0.6623 \| 0.9914 \| \| 0.0101 \| 9.04 \| 13500 \| 0.0451 \| 0.6691 \| 0.6606 \| 0.6648 \| 0.9913 \| \| 0.0099 \| 9.38 \| 14000 \| 0.0462 \| 0.7006 \| 0.6826 \| 0.6914 \| 0.9918 \| \| 0.0107 \| 9.71 \| 14500 \| 0.0444 \| 0.6840 \| 0.6752 \| 0.6796 \| 0.9915 \| \| 0.0118 \| 10.05 \| 15000 \| 0.0457 \| 0.7015 \| 0.6771 \| 0.6891 \| 0.9918 \| \| 0.0102 \| 10.38 \| 15500 \| 0.0500 \| 0.7413 \| 0.6679 \| 0.7027 \| 0.9919 \| \| 0.0107 \| 10.72 \| 16000 \| 0.0470 \| 0.7319 \| 0.7064 \| 0.7190 \| 0.9920 \| ### Framework versions - Transformers 4.18.0 - Pytorch 1.10.0+cu111 - Datasets 2.0.0 - Tokenizers 0.11.6
Nadhiya/distilbert-base-uncased-finetuned-squad	Nadhiya	2022-04-29T18:20:29Z	14	0	transformers	[ "transformers", "pytorch", "tensorboard", "distilbert", "question-answering", "generated_from_trainer", "license:apache-2.0", "endpoints_compatible", "region:us" ]	question-answering	2022-04-24T20:58:37Z	--- license: apache-2.0 tags: - generated_from_trainer model-index: - name: distilbert-base-uncased-finetuned-squad results: [] --- <!-- This model card has been generated automatically according to the information the Trainer had access to. You should probably proofread and complete it, then remove this comment. --> # distilbert-base-uncased-finetuned-squad This model is a fine-tuned version of [distilbert-base-uncased](https://huggingface.co/distilbert-base-uncased) on an unknown dataset. It achieves the following results on the evaluation set: - Loss: 6.6023 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 2e-05 - train_batch_size: 16 - eval_batch_size: 16 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - num_epochs: 3 ### Training results \| Training Loss \| Epoch \| Step \| Validation Loss \| \|:-------------:\|:-----:\|:----:\|:---------------:\| \| No log \| 1.0 \| 54 \| 5.8535 \| \| No log \| 2.0 \| 108 \| 6.4469 \| \| No log \| 3.0 \| 162 \| 6.6023 \| ### Framework versions - Transformers 4.18.0 - Pytorch 1.11.0+cu113 - Datasets 2.1.0 - Tokenizers 0.12.1
nikhedward/bart-large-cnn-finetuned-multi-news	nikhedward	2022-04-29T15:22:47Z	14	2	transformers	[ "transformers", "pytorch", "tensorboard", "bart", "text2text-generation", "generated_from_trainer", "dataset:multi_news", "license:mit", "model-index", "autotrain_compatible", "endpoints_compatible", "region:us" ]	text2text-generation	2022-04-13T04:36:34Z	--- license: mit tags: - generated_from_trainer datasets: - multi_news metrics: - rouge model-index: - name: bart-large-cnn-finetuned-multi-news results: - task: name: Sequence-to-sequence Language Modeling type: text2text-generation dataset: name: multi_news type: multi_news args: default metrics: - name: Rouge1 type: rouge value: 42.0423 --- <!-- This model card has been generated automatically according to the information the Trainer had access to. You should probably proofread and complete it, then remove this comment. --> # bart-large-cnn-finetuned-multi-news This model is a fine-tuned version of [facebook/bart-large-cnn](https://huggingface.co/facebook/bart-large-cnn) on the multi_news dataset. It achieves the following results on the evaluation set: - Loss: 2.0950 - Rouge1: 42.0423 - Rouge2: 14.8812 - Rougel: 23.3412 - Rougelsum: 36.2613 ## Model description bart-large-cnn fine tuned on sample of multi-news dataset ## Intended uses & limitations The intended use of the model is for downstream summarization tasks but it's limited to input text 1024 words. Any text longer than that would be truncated. ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 2e-05 - train_batch_size: 4 - eval_batch_size: 4 - seed: 42 - gradient_accumulation_steps: 4 - total_train_batch_size: 16 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - num_epochs: 1 ### Training results \| Training Loss \| Epoch \| Step \| Validation Loss \| Rouge1 \| Rouge2 \| Rougel \| Rougelsum \| \|:-------------:\|:-----:\|:----:\|:---------------:\|:-------:\|:-------:\|:-------:\|:---------:\| \| 2.2037 \| 1.0 \| 750 \| 2.0950 \| 42.0423 \| 14.8812 \| 23.3412 \| 36.2613 \| ### Framework versions - Transformers 4.18.0 - Pytorch 1.10.0+cu111 - Datasets 2.0.0 - Tokenizers 0.11.6
huggingtweets/cokedupoptions-greg16676935420-parikpatelcfa	huggingtweets	2022-04-29T15:09:43Z	3	0	transformers	[ "transformers", "pytorch", "gpt2", "text-generation", "huggingtweets", "en", "autotrain_compatible", "text-generation-inference", "endpoints_compatible", "region:us" ]	text-generation	2022-04-29T07:44:08Z	--- language: en thumbnail: https://github.com/borisdayma/huggingtweets/blob/master/img/logo.png?raw=true tags: - huggingtweets widget: - text: "My dream is" --- <div class="inline-flex flex-col" style="line-height: 1.5;"> <div class="flex"> <div style="display:inherit; margin-left: 4px; margin-right: 4px; width: 92px; height:92px; border-radius: 50%; background-size: cover; background-image: url('https://pbs.twimg.com/profile_images/1514648481281056772/ACunKh0I_400x400.jpg')"> </div> <div style="display:inherit; margin-left: 4px; margin-right: 4px; width: 92px; height:92px; border-radius: 50%; background-size: cover; background-image: url('https://pbs.twimg.com/profile_images/1484924573032148993/qdB7hbSU_400x400.jpg')"> </div> <div style="display:inherit; margin-left: 4px; margin-right: 4px; width: 92px; height:92px; border-radius: 50%; background-size: cover; background-image: url('https://pbs.twimg.com/profile_images/1341030286386192386/TzEiVCaJ_400x400.jpg')"> </div> </div> <div style="text-align: center; margin-top: 3px; font-size: 16px; font-weight: 800">🤖 AI CYBORG 🤖</div> <div style="text-align: center; font-size: 16px; font-weight: 800">greg & John W. Rich (Fake Tech Exec) & Dr. Parik Patel, BA, CFA, ACCA Esq. (drpatel.eth)</div> <div style="text-align: center; font-size: 14px;">@cokedupoptions-greg16676935420-parikpatelcfa</div> </div> I was made with [huggingtweets](https://github.com/borisdayma/huggingtweets). Create your own bot based on your favorite user with [the demo](https://colab.research.google.com/github/borisdayma/huggingtweets/blob/master/huggingtweets-demo.ipynb)! ## How does it work? The model uses the following pipeline. ![pipeline](https://github.com/borisdayma/huggingtweets/blob/master/img/pipeline.png?raw=true) To understand how the model was developed, check the [W&B report](https://wandb.ai/wandb/huggingtweets/reports/HuggingTweets-Train-a-Model-to-Generate-Tweets--VmlldzoxMTY5MjI). ## Training data The model was trained on tweets from greg & John W. Rich (Fake Tech Exec) & Dr. Parik Patel, BA, CFA, ACCA Esq. (drpatel.eth). \| Data \| greg \| John W. Rich (Fake Tech Exec) \| Dr. Parik Patel, BA, CFA, ACCA Esq. (drpatel.eth) \| \| --- \| --- \| --- \| --- \| \| Tweets downloaded \| 3247 \| 3247 \| 3250 \| \| Retweets \| 27 \| 202 \| 22 \| \| Short tweets \| 664 \| 331 \| 719 \| \| Tweets kept \| 2556 \| 2714 \| 2509 \| [Explore the data](https://wandb.ai/wandb/huggingtweets/runs/snhk0760/artifacts), which is tracked with [W&B artifacts](https://docs.wandb.com/artifacts) at every step of the pipeline. ## Training procedure The model is based on a pre-trained [GPT-2](https://huggingface.co/gpt2) which is fine-tuned on @cokedupoptions-greg16676935420-parikpatelcfa's tweets. Hyperparameters and metrics are recorded in the [W&B training run](https://wandb.ai/wandb/huggingtweets/runs/iresidwo) for full transparency and reproducibility. At the end of training, [the final model](https://wandb.ai/wandb/huggingtweets/runs/iresidwo/artifacts) is logged and versioned. ## How to use You can use this model directly with a pipeline for text generation: ```python from transformers import pipeline generator = pipeline('text-generation', model='huggingtweets/cokedupoptions-greg16676935420-parikpatelcfa') generator("My dream is", num_return_sequences=5) ``` ## Limitations and bias The model suffers from [the same limitations and bias as GPT-2](https://huggingface.co/gpt2#limitations-and-bias). In addition, the data present in the user's tweets further affects the text generated by the model. ## About Built by Boris Dayma [![Follow](https://img.shields.io/twitter/follow/borisdayma?style=social)](https://twitter.com/intent/follow?screen_name=borisdayma) For more details, visit the project repository. [![GitHub stars](https://img.shields.io/github/stars/borisdayma/huggingtweets?style=social)](https://github.com/borisdayma/huggingtweets)
Goud/DarijaBERT-summarization-goud	Goud	2022-04-29T15:07:03Z	17	2	transformers	[ "transformers", "pytorch", "encoder-decoder", "text2text-generation", "summarization", "dataset:Goud/Goud-sum", "autotrain_compatible", "endpoints_compatible", "region:us" ]	summarization	2022-04-20T22:37:47Z	--- datasets: - Goud/Goud-sum language: - "Moroccan Arabic (MA)" - "Modern Standard Arabic (MSA)" metrics: - rouge tags: - summarization widget: - text: "توصل الاتحاد الأوروبي، في وقت مبكر من اليوم السبت، إلى اتفاق تاريخي يستهدف خطاب الكراهية والمعلومات المضللة والمحتويات الضارة الأخرى الموجودة على شبكة الإنترنيت. وحسب تقارير صحفية، سيجبر القانون شركات التكنولوجيا الكبرى على مراقبة نفسها بشكل أكثر صرامة، ويسهل على المستخدمين الإبلاغ عن المشاكل، ويمكن الاتفاق المنظمين من معاقبة الشركات غير الممتثلة بغرامات تقدر بالملايير. ويركز الاتفاق على قواعد جديدة تتطلب من شركات التكنولوجيا العملاقة بذل المزيد من الجهد لمراقبة المحتوى على منصاتها ودفع رسوم للجهات المنظمة التي تراقب مدى امتثالها. ويعد قانون الخدمات الرقمية الشق الثاني من إستراتيجية المفوضة الأوروبية لشؤون المنافسة، مارغريت فيستاغر، للحد من هيمنة وحدة غوغل التابعة لألفابت، وميتا (فيسبوك سابقا) وغيرهما من شركات التكنولوجيا الأمريكية العملاقة. وقالت فيستاغر في تغريدة “توصلنا إلى اتفاق بشأن قانون الخدمات الرقمية، موضحة أن القانون سيضمن أن ما يعتبر غير قانوني في حالة عدم الاتصال بالشبكة ينظر إليه أيضا ويتم التعامل معه على أنه غير قانوني عبر الشبكة (الإنترنت) – ليس كشعار (ولكن) كواقع”. وتواجه الشركات بموجب قانون الخدمات الرقمية غرامات تصل إلى 6 في المائة من إجمالي عملياتها على مستوى العالم لانتهاك القواعد بينما قد تؤدي الانتهاكات المتكررة إلى حظرها من ممارسة أعمالها في الاتحاد الأوروبي. وأيدت دول الاتحاد والمشرعون الشهر الماضي القواعد التي طرحتها فيستاغر والمسماة قانون الأسواق الرقمية التي قد تجبر غوغل وأمازون وأبل وميتا وميكروسوفت على تغيير ممارساتها الأساسية في أوروبا. " --- This model was introduced in [this paper](https://openreview.net/forum?id=BMVq5MELb9). It is an encoder-decoder model that was initialized with [DarijaBERT](https://huggingface.co/Kamel/DarijaBERT) checkpoint. The model is finetuned for text summarization on [Goud dataset](https://huggingface.co/datasets/Goud/Goud-sum). ## How to use This is how you can use this model ```python from transformers import EncoderDecoderModel, BertTokenizer article = """توصل الاتحاد الأوروبي، في وقت مبكر من اليوم السبت، إلى اتفاق تاريخي يستهدف خطاب الكراهية والمعلومات المضللة والمحتويات الضارة الأخرى الموجودة على شبكة الإنترنيت. وحسب تقارير صحفية، سيجبر القانون شركات التكنولوجيا الكبرى على مراقبة نفسها بشكل أكثر صرامة، ويسهل على المستخدمين الإبلاغ عن المشاكل، ويمكن الاتفاق المنظمين من معاقبة الشركات غير الممتثلة بغرامات تقدر بالملايير. ويركز الاتفاق على قواعد جديدة تتطلب من شركات التكنولوجيا العملاقة بذل المزيد من الجهد لمراقبة المحتوى على منصاتها ودفع رسوم للجهات المنظمة التي تراقب مدى امتثالها. ويعد قانون الخدمات الرقمية الشق الثاني من إستراتيجية المفوضة الأوروبية لشؤون المنافسة، مارغريت فيستاغر، للحد من هيمنة وحدة غوغل التابعة لألفابت، وميتا (فيسبوك سابقا) وغيرهما من شركات التكنولوجيا الأمريكية العملاقة. وقالت فيستاغر في تغريدة “توصلنا إلى اتفاق بشأن قانون الخدمات الرقمية، موضحة أن القانون سيضمن أن ما يعتبر غير قانوني في حالة عدم الاتصال بالشبكة ينظر إليه أيضا ويتم التعامل معه على أنه غير قانوني عبر الشبكة (الإنترنت) – ليس كشعار (ولكن) كواقع”. وتواجه الشركات بموجب قانون الخدمات الرقمية غرامات تصل إلى 6 في المائة من إجمالي عملياتها على مستوى العالم لانتهاك القواعد بينما قد تؤدي الانتهاكات المتكررة إلى حظرها من ممارسة أعمالها في الاتحاد الأوروبي. وأيدت دول الاتحاد والمشرعون الشهر الماضي القواعد التي طرحتها فيستاغر والمسماة قانون الأسواق الرقمية التي قد تجبر غوغل وأمازون وأبل وميتا وميكروسوفت على تغيير ممارساتها الأساسية في أوروبا. """ tokenizer = BertTokenizer.from_pretrained("Goud/DarijaBERT-summarization-goud") model = EncoderDecoderModel.from_pretrained("Goud/DarijaBERT-summarization-goud") input_ids = tokenizer(article, return_tensors="pt", truncation=True, padding=True).input_ids generated = model.generate(input_ids)[0] output = tokenizer.decode(generated, skip_special_tokens=True) ``` ## Citation Information ``` @inproceedings{issam2022goudma, title={Goud.ma: a News Article Dataset for Summarization in Moroccan Darija}, author={Abderrahmane Issam and Khalil Mrini}, booktitle={3rd Workshop on African Natural Language Processing}, year={2022}, url={https://openreview.net/forum?id=BMVq5MELb9} } ```
Goud/DziriBERT-summarization-goud	Goud	2022-04-29T15:06:30Z	14	2	transformers	[ "transformers", "pytorch", "encoder-decoder", "text2text-generation", "summarization", "dataset:Goud/Goud-sum", "autotrain_compatible", "endpoints_compatible", "region:us" ]	summarization	2022-04-20T22:16:15Z	--- datasets: - Goud/Goud-sum language: - "Moroccan Arabic (MA)" - "Modern Standard Arabic (MSA)" metrics: - rouge tags: - summarization widget: - text: "توصل الاتحاد الأوروبي، في وقت مبكر من اليوم السبت، إلى اتفاق تاريخي يستهدف خطاب الكراهية والمعلومات المضللة والمحتويات الضارة الأخرى الموجودة على شبكة الإنترنيت. وحسب تقارير صحفية، سيجبر القانون شركات التكنولوجيا الكبرى على مراقبة نفسها بشكل أكثر صرامة، ويسهل على المستخدمين الإبلاغ عن المشاكل، ويمكن الاتفاق المنظمين من معاقبة الشركات غير الممتثلة بغرامات تقدر بالملايير. ويركز الاتفاق على قواعد جديدة تتطلب من شركات التكنولوجيا العملاقة بذل المزيد من الجهد لمراقبة المحتوى على منصاتها ودفع رسوم للجهات المنظمة التي تراقب مدى امتثالها. ويعد قانون الخدمات الرقمية الشق الثاني من إستراتيجية المفوضة الأوروبية لشؤون المنافسة، مارغريت فيستاغر، للحد من هيمنة وحدة غوغل التابعة لألفابت، وميتا (فيسبوك سابقا) وغيرهما من شركات التكنولوجيا الأمريكية العملاقة. وقالت فيستاغر في تغريدة “توصلنا إلى اتفاق بشأن قانون الخدمات الرقمية، موضحة أن القانون سيضمن أن ما يعتبر غير قانوني في حالة عدم الاتصال بالشبكة ينظر إليه أيضا ويتم التعامل معه على أنه غير قانوني عبر الشبكة (الإنترنت) – ليس كشعار (ولكن) كواقع”. وتواجه الشركات بموجب قانون الخدمات الرقمية غرامات تصل إلى 6 في المائة من إجمالي عملياتها على مستوى العالم لانتهاك القواعد بينما قد تؤدي الانتهاكات المتكررة إلى حظرها من ممارسة أعمالها في الاتحاد الأوروبي. وأيدت دول الاتحاد والمشرعون الشهر الماضي القواعد التي طرحتها فيستاغر والمسماة قانون الأسواق الرقمية التي قد تجبر غوغل وأمازون وأبل وميتا وميكروسوفت على تغيير ممارساتها الأساسية في أوروبا. " --- This model was introduced in [this paper](https://openreview.net/forum?id=BMVq5MELb9). It is an encoder-decoder model that was initialized with [DziriBERT](https://huggingface.co/alger-ia/dziribert) checkpoint. The model is finetuned for text summarization on [Goud dataset](https://huggingface.co/datasets/Goud/Goud-sum). ## How to use This is how you can use this model ```python from transformers import EncoderDecoderModel, BertTokenizer article = """توصل الاتحاد الأوروبي، في وقت مبكر من اليوم السبت، إلى اتفاق تاريخي يستهدف خطاب الكراهية والمعلومات المضللة والمحتويات الضارة الأخرى الموجودة على شبكة الإنترنيت. وحسب تقارير صحفية، سيجبر القانون شركات التكنولوجيا الكبرى على مراقبة نفسها بشكل أكثر صرامة، ويسهل على المستخدمين الإبلاغ عن المشاكل، ويمكن الاتفاق المنظمين من معاقبة الشركات غير الممتثلة بغرامات تقدر بالملايير. ويركز الاتفاق على قواعد جديدة تتطلب من شركات التكنولوجيا العملاقة بذل المزيد من الجهد لمراقبة المحتوى على منصاتها ودفع رسوم للجهات المنظمة التي تراقب مدى امتثالها. ويعد قانون الخدمات الرقمية الشق الثاني من إستراتيجية المفوضة الأوروبية لشؤون المنافسة، مارغريت فيستاغر، للحد من هيمنة وحدة غوغل التابعة لألفابت، وميتا (فيسبوك سابقا) وغيرهما من شركات التكنولوجيا الأمريكية العملاقة. وقالت فيستاغر في تغريدة “توصلنا إلى اتفاق بشأن قانون الخدمات الرقمية، موضحة أن القانون سيضمن أن ما يعتبر غير قانوني في حالة عدم الاتصال بالشبكة ينظر إليه أيضا ويتم التعامل معه على أنه غير قانوني عبر الشبكة (الإنترنت) – ليس كشعار (ولكن) كواقع”. وتواجه الشركات بموجب قانون الخدمات الرقمية غرامات تصل إلى 6 في المائة من إجمالي عملياتها على مستوى العالم لانتهاك القواعد بينما قد تؤدي الانتهاكات المتكررة إلى حظرها من ممارسة أعمالها في الاتحاد الأوروبي. وأيدت دول الاتحاد والمشرعون الشهر الماضي القواعد التي طرحتها فيستاغر والمسماة قانون الأسواق الرقمية التي قد تجبر غوغل وأمازون وأبل وميتا وميكروسوفت على تغيير ممارساتها الأساسية في أوروبا. """ tokenizer = BertTokenizer.from_pretrained("Goud/DziriBERT-summarization-goud") model = EncoderDecoderModel.from_pretrained("Goud/DziriBERT-summarization-goud") input_ids = tokenizer(article, return_tensors="pt", truncation=True, padding=True).input_ids generated = model.generate(input_ids)[0] output = tokenizer.decode(generated, skip_special_tokens=True) ``` ## Citation Information ``` @inproceedings{issam2022goudma, title={Goud.ma: a News Article Dataset for Summarization in Moroccan Darija}, author={Abderrahmane Issam and Khalil Mrini}, booktitle={3rd Workshop on African Natural Language Processing}, year={2022}, url={https://openreview.net/forum?id=BMVq5MELb9} } ```
KoboldAI/GPT-Neo-125M-AID	KoboldAI	2022-04-29T14:48:16Z	19	1	transformers	[ "transformers", "pytorch", "gpt_neo", "text-generation", "autotrain_compatible", "endpoints_compatible", "region:us" ]	text-generation	2022-03-02T23:29:04Z	# GPT-Neo-125M-AID This model was finetuned by Henk717 on Google Colab, it contains text adventure tuning and its the smallest 'Adventure' model of its size. Because of its limited size the behavior is mostly suitable for testing text adventure gamemodes at fast speeds, for a coherent adventure you are better off using one of the 2.7B models.
faisalahmad2/autotrain-nlp-text-summarization-by-faisal-793224456	faisalahmad2	2022-04-29T14:05:30Z	3	0	transformers	[ "transformers", "pytorch", "t5", "text2text-generation", "autotrain", "en", "dataset:faisalahmad2/autotrain-data-nlp-text-summarization-by-faisal", "co2_eq_emissions", "autotrain_compatible", "text-generation-inference", "endpoints_compatible", "region:us" ]	text2text-generation	2022-04-27T15:03:43Z	--- tags: autotrain language: en widget: - text: "I love AutoTrain 🤗" datasets: - faisalahmad2/autotrain-data-nlp-text-summarization-by-faisal co2_eq_emissions: 27.26671996544415 --- # Model Trained Using AutoTrain - Problem type: Summarization - Model ID: 793224456 - CO2 Emissions (in grams): 27.26671996544415 ## Validation Metrics - Loss: 1.5189369916915894 - Rouge1: 38.7852 - Rouge2: 17.0785 - RougeL: 32.1082 - RougeLsum: 32.1103 - Gen Len: 18.7332 ## Usage You can use cURL to access this model: ``` $ curl -X POST -H "Authorization: Bearer YOUR_HUGGINGFACE_API_KEY" -H "Content-Type: application/json" -d '{"inputs": "I love AutoTrain"}' https://api-inference.huggingface.co/faisalahmad2/autotrain-nlp-text-summarization-by-faisal-793224456 ```
huggingtweets/corpsecrusader	huggingtweets	2022-04-29T13:57:10Z	4	0	transformers	[ "transformers", "pytorch", "gpt2", "text-generation", "huggingtweets", "en", "autotrain_compatible", "text-generation-inference", "endpoints_compatible", "region:us" ]	text-generation	2022-03-02T23:29:05Z	--- language: en thumbnail: http://www.huggingtweets.com/corpsecrusader/1651240626010/predictions.png tags: - huggingtweets widget: - text: "My dream is" --- <div class="inline-flex flex-col" style="line-height: 1.5;"> <div class="flex"> <div style="display:inherit; margin-left: 4px; margin-right: 4px; width: 92px; height:92px; border-radius: 50%; background-size: cover; background-image: url('https://pbs.twimg.com/profile_images/1515787050334801925/tyxpMmj1_400x400.jpg')"> </div> <div style="display:none; margin-left: 4px; margin-right: 4px; width: 92px; height:92px; border-radius: 50%; background-size: cover; background-image: url('')"> </div> <div style="display:none; margin-left: 4px; margin-right: 4px; width: 92px; height:92px; border-radius: 50%; background-size: cover; background-image: url('')"> </div> </div> <div style="text-align: center; margin-top: 3px; font-size: 16px; font-weight: 800">🤖 AI BOT 🤖</div> <div style="text-align: center; font-size: 16px; font-weight: 800">Corpse Crusader 🫀🇫🇮 gamedev hours🧱🍐💨💪</div> <div style="text-align: center; font-size: 14px;">@corpsecrusader</div> </div> I was made with [huggingtweets](https://github.com/borisdayma/huggingtweets). Create your own bot based on your favorite user with [the demo](https://colab.research.google.com/github/borisdayma/huggingtweets/blob/master/huggingtweets-demo.ipynb)! ## How does it work? The model uses the following pipeline. ![pipeline](https://github.com/borisdayma/huggingtweets/blob/master/img/pipeline.png?raw=true) To understand how the model was developed, check the [W&B report](https://wandb.ai/wandb/huggingtweets/reports/HuggingTweets-Train-a-Model-to-Generate-Tweets--VmlldzoxMTY5MjI). ## Training data The model was trained on tweets from Corpse Crusader 🫀🇫🇮 gamedev hours🧱🍐💨💪. \| Data \| Corpse Crusader 🫀🇫🇮 gamedev hours🧱🍐💨💪 \| \| --- \| --- \| \| Tweets downloaded \| 3244 \| \| Retweets \| 405 \| \| Short tweets \| 658 \| \| Tweets kept \| 2181 \| [Explore the data](https://wandb.ai/wandb/huggingtweets/runs/ogdqtie2/artifacts), which is tracked with [W&B artifacts](https://docs.wandb.com/artifacts) at every step of the pipeline. ## Training procedure The model is based on a pre-trained [GPT-2](https://huggingface.co/gpt2) which is fine-tuned on @corpsecrusader's tweets. Hyperparameters and metrics are recorded in the [W&B training run](https://wandb.ai/wandb/huggingtweets/runs/1ecpg08j) for full transparency and reproducibility. At the end of training, [the final model](https://wandb.ai/wandb/huggingtweets/runs/1ecpg08j/artifacts) is logged and versioned. ## How to use You can use this model directly with a pipeline for text generation: ```python from transformers import pipeline generator = pipeline('text-generation', model='huggingtweets/corpsecrusader') generator("My dream is", num_return_sequences=5) ``` ## Limitations and bias The model suffers from [the same limitations and bias as GPT-2](https://huggingface.co/gpt2#limitations-and-bias). In addition, the data present in the user's tweets further affects the text generated by the model. ## About Built by Boris Dayma [![Follow](https://img.shields.io/twitter/follow/borisdayma?style=social)](https://twitter.com/intent/follow?screen_name=borisdayma) For more details, visit the project repository. [![GitHub stars](https://img.shields.io/github/stars/borisdayma/huggingtweets?style=social)](https://github.com/borisdayma/huggingtweets)
Ansh/keras-demo	Ansh	2022-04-29T13:48:51Z	1	0	keras	[ "keras", "tf-keras", "bert", "region:us" ]	null	2022-04-29T12:55:31Z	--- library_name: keras --- ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - optimizer: {'name': 'Adam', 'learning_rate': 1e-05, 'decay': 1e-07, 'beta_1': 0.9, 'beta_2': 0.999, 'epsilon': 1e-07, 'amsgrad': False} - training_precision: float32 ## Training Metrics Model history needed ## Model Plot <details> <summary>View Model Plot</summary> ![Model Image](./model.png) </details>
umarkhalid96/t5-small-train	umarkhalid96	2022-04-29T12:36:08Z	9	0	transformers	[ "transformers", "pytorch", "tensorboard", "t5", "text2text-generation", "summarization", "generated_from_trainer", "license:apache-2.0", "autotrain_compatible", "text-generation-inference", "endpoints_compatible", "region:us" ]	summarization	2022-04-24T19:52:13Z	--- license: apache-2.0 tags: - summarization - generated_from_trainer metrics: - rouge model-index: - name: t5-small-train results: [] --- <!-- This model card has been generated automatically according to the information the Trainer had access to. You should probably proofread and complete it, then remove this comment. --> # t5-small-train This model is a fine-tuned version of [t5-small](https://huggingface.co/t5-small) on an unknown dataset. It achieves the following results on the evaluation set: - Loss: 2.2669 - Rouge1: 43.2372 - Rouge2: 21.6755 - Rougel: 38.1637 - Rougelsum: 38.5444 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 5.6e-05 - train_batch_size: 8 - eval_batch_size: 8 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - num_epochs: 8 ### Training results \| Training Loss \| Epoch \| Step \| Validation Loss \| Rouge1 \| Rouge2 \| Rougel \| Rougelsum \| \|:-------------:\|:-----:\|:----:\|:---------------:\|:-------:\|:-------:\|:-------:\|:---------:\| \| 3.2032 \| 1.0 \| 45 \| 2.6305 \| 34.393 \| 15.4821 \| 30.3601 \| 30.5865 \| \| 2.6291 \| 2.0 \| 90 \| 2.4169 \| 38.2327 \| 18.4622 \| 34.2887 \| 34.3385 \| \| 2.4294 \| 3.0 \| 135 \| 2.3395 \| 40.4405 \| 19.927 \| 36.559 \| 36.8095 \| \| 2.3191 \| 4.0 \| 180 \| 2.3059 \| 41.4214 \| 20.4534 \| 36.6399 \| 36.9088 \| \| 2.2949 \| 5.0 \| 225 \| 2.2857 \| 42.6906 \| 21.1492 \| 37.5557 \| 37.8722 \| \| 2.2591 \| 6.0 \| 270 \| 2.2762 \| 43.1598 \| 21.6179 \| 38.1235 \| 38.5053 \| \| 2.1722 \| 7.0 \| 315 \| 2.2680 \| 43.4447 \| 21.8048 \| 38.4077 \| 38.7384 \| \| 2.1993 \| 8.0 \| 360 \| 2.2669 \| 43.2372 \| 21.6755 \| 38.1637 \| 38.5444 \| ### Framework versions - Transformers 4.18.0 - Pytorch 1.11.0+cu113 - Datasets 2.1.0 - Tokenizers 0.12.1
nepp1d0/SingleBertModel-ProtBertfinetuned-smilesBindingDB	nepp1d0	2022-04-29T12:23:55Z	5	0	transformers	[ "transformers", "pytorch", "tensorboard", "bert", "fill-mask", "generated_from_trainer", "autotrain_compatible", "endpoints_compatible", "region:us" ]	fill-mask	2022-04-13T22:27:57Z	--- tags: - generated_from_trainer model-index: - name: SingleBertModel-ProtBertfinetuned-smilesBindingDB results: [] --- <!-- This model card has been generated automatically according to the information the Trainer had access to. You should probably proofread and complete it, then remove this comment. --> # SingleBertModel-ProtBertfinetuned-smilesBindingDB This model is a fine-tuned version of [Rostlab/prot_bert](https://huggingface.co/Rostlab/prot_bert) on an unknown dataset. It achieves the following results on the evaluation set: - Loss: nan ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 0.0001 - train_batch_size: 1 - eval_batch_size: 1 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - num_epochs: 5 - mixed_precision_training: Native AMP ### Training results \| Training Loss \| Epoch \| Step \| Validation Loss \| \|:-------------:\|:-----:\|:-----:\|:---------------:\| \| 2.5245 \| 1.0 \| 10000 \| nan \| \| 2.5037 \| 2.0 \| 20000 \| nan \| \| 2.4967 \| 3.0 \| 30000 \| nan \| \| 2.4983 \| 4.0 \| 40000 \| nan \| \| 2.4926 \| 5.0 \| 50000 \| nan \| ### Framework versions - Transformers 4.18.0 - Pytorch 1.11.0+cu113 - Datasets 2.1.0 - Tokenizers 0.12.1
doc2query/msmarco-russian-mt5-base-v1	doc2query	2022-04-29T12:10:29Z	21	8	transformers	[ "transformers", "pytorch", "mt5", "text2text-generation", "ru", "dataset:unicamp-dl/mmarco", "arxiv:1904.08375", "arxiv:2104.08663", "arxiv:2112.07577", "license:apache-2.0", "autotrain_compatible", "endpoints_compatible", "region:us" ]	text2text-generation	2022-04-29T12:10:14Z	--- language: ru datasets: - unicamp-dl/mmarco widget: - text: "Python (МФА: [ˈpʌɪθ(ə)n]; в русском языке встречаются названия пито́н или па́йтон) — высокоуровневый язык программирования общего назначения с динамической строгой типизацией и автоматическим управлением памятью, ориентированный на повышение производительности разработчика, читаемости кода и его качества, а также на обеспечение переносимости написанных на нём программ." license: apache-2.0 --- # doc2query/msmarco-russian-mt5-base-v1 This is a [doc2query](https://arxiv.org/abs/1904.08375) model based on mT5 (also known as [docT5query](https://cs.uwaterloo.ca/~jimmylin/publications/Nogueira_Lin_2019_docTTTTTquery-v2.pdf)). It can be used for: - Document expansion: You generate for your paragraphs 20-40 queries and index the paragraphs and the generates queries in a standard BM25 index like Elasticsearch, OpenSearch, or Lucene. The generated queries help to close the lexical gap of lexical search, as the generate queries contain synonyms. Further, it re-weights words giving important words a higher weight even if they appear seldomn in a paragraph. In our [BEIR](https://arxiv.org/abs/2104.08663) paper we showed that BM25+docT5query is a powerful search engine. In the [BEIR repository](https://github.com/beir-cellar/beir) we have an example how to use docT5query with Pyserini. - Domain Specific Training Data Generation: It can be used to generate training data to learn an embedding model. In our [GPL-Paper](https://arxiv.org/abs/2112.07577) / [GPL Example on SBERT.net](https://www.sbert.net/examples/domain_adaptation/README.html#gpl-generative-pseudo-labeling) we have an example how to use the model to generate (query, text) pairs for a given collection of unlabeled texts. These pairs can then be used to train powerful dense embedding models. ## Usage ```python from transformers import AutoTokenizer, AutoModelForSeq2SeqLM import torch model_name = 'doc2query/msmarco-russian-mt5-base-v1' tokenizer = AutoTokenizer.from_pretrained(model_name) model = AutoModelForSeq2SeqLM.from_pretrained(model_name) text = "Python (МФА: [ˈpʌɪθ(ə)n]; в русском языке встречаются названия пито́н или па́йтон) — высокоуровневый язык программирования общего назначения с динамической строгой типизацией и автоматическим управлением памятью, ориентированный на повышение производительности разработчика, читаемости кода и его качества, а также на обеспечение переносимости написанных на нём программ." def create_queries(para): input_ids = tokenizer.encode(para, return_tensors='pt') with torch.no_grad(): # Here we use top_k / top_k random sampling. It generates more diverse queries, but of lower quality sampling_outputs = model.generate( input_ids=input_ids, max_length=64, do_sample=True, top_p=0.95, top_k=10, num_return_sequences=5 ) # Here we use Beam-search. It generates better quality queries, but with less diversity beam_outputs = model.generate( input_ids=input_ids, max_length=64, num_beams=5, no_repeat_ngram_size=2, num_return_sequences=5, early_stopping=True ) print("Paragraph:") print(para) print("\nBeam Outputs:") for i in range(len(beam_outputs)): query = tokenizer.decode(beam_outputs[i], skip_special_tokens=True) print(f'{i + 1}: {query}') print("\nSampling Outputs:") for i in range(len(sampling_outputs)): query = tokenizer.decode(sampling_outputs[i], skip_special_tokens=True) print(f'{i + 1}: {query}') create_queries(text) ``` Note: `model.generate()` is non-deterministic for top_k/top_n sampling. It produces different queries each time you run it. ## Training This model fine-tuned [google/mt5-base](https://huggingface.co/google/mt5-base) for 66k training steps (4 epochs on the 500k training pairs from MS MARCO). For the training script, see the `train_script.py` in this repository. The input-text was truncated to 320 word pieces. Output text was generated up to 64 word pieces. This model was trained on a (query, passage) from the [mMARCO dataset](https://github.com/unicamp-dl/mMARCO).
huggan/stylegan_cat256	huggan	2022-04-29T12:01:40Z	0	1	null	[ "pytorch", "gan", "stylegan", "huggan", "unconditional-image-generation", "license:apache-2.0", "region:us" ]	unconditional-image-generation	2022-04-18T21:54:15Z	--- tags: - gan - stylegan - huggan - unconditional-image-generation license: apache-2.0 --- The model provided is a StyleGAN generator trained on the LSUN cats dataset with a resolution of 256px. It is uploaded as part of porting this project: https://github.com/genforce/sefa to hugginface spaces.
doc2query/msmarco-indonesian-mt5-base-v1	doc2query	2022-04-29T11:58:59Z	23	2	transformers	[ "transformers", "pytorch", "mt5", "text2text-generation", "id", "dataset:unicamp-dl/mmarco", "arxiv:1904.08375", "arxiv:2104.08663", "arxiv:2112.07577", "license:apache-2.0", "autotrain_compatible", "endpoints_compatible", "region:us" ]	text2text-generation	2022-04-29T11:58:44Z	--- language: id datasets: - unicamp-dl/mmarco widget: - text: "Python adalah bahasa pemrograman tujuan umum yang ditafsirkan, tingkat tinggi. Dibuat oleh Guido van Rossum dan pertama kali dirilis pada tahun 1991, filosofi desain Python menekankan keterbacaan kode dengan penggunaan spasi putih yang signifikan. Konstruksi bahasanya dan pendekatan berorientasi objek bertujuan untuk membantu pemrogram menulis kode yang jelas dan logis untuk proyek skala kecil dan besar." license: apache-2.0 --- # doc2query/msmarco-indonesian-mt5-base-v1 This is a [doc2query](https://arxiv.org/abs/1904.08375) model based on mT5 (also known as [docT5query](https://cs.uwaterloo.ca/~jimmylin/publications/Nogueira_Lin_2019_docTTTTTquery-v2.pdf)). It can be used for: - Document expansion: You generate for your paragraphs 20-40 queries and index the paragraphs and the generates queries in a standard BM25 index like Elasticsearch, OpenSearch, or Lucene. The generated queries help to close the lexical gap of lexical search, as the generate queries contain synonyms. Further, it re-weights words giving important words a higher weight even if they appear seldomn in a paragraph. In our [BEIR](https://arxiv.org/abs/2104.08663) paper we showed that BM25+docT5query is a powerful search engine. In the [BEIR repository](https://github.com/beir-cellar/beir) we have an example how to use docT5query with Pyserini. - Domain Specific Training Data Generation: It can be used to generate training data to learn an embedding model. In our [GPL-Paper](https://arxiv.org/abs/2112.07577) / [GPL Example on SBERT.net](https://www.sbert.net/examples/domain_adaptation/README.html#gpl-generative-pseudo-labeling) we have an example how to use the model to generate (query, text) pairs for a given collection of unlabeled texts. These pairs can then be used to train powerful dense embedding models. ## Usage ```python from transformers import AutoTokenizer, AutoModelForSeq2SeqLM import torch model_name = 'doc2query/msmarco-indonesian-mt5-base-v1' tokenizer = AutoTokenizer.from_pretrained(model_name) model = AutoModelForSeq2SeqLM.from_pretrained(model_name) text = "Python adalah bahasa pemrograman tujuan umum yang ditafsirkan, tingkat tinggi. Dibuat oleh Guido van Rossum dan pertama kali dirilis pada tahun 1991, filosofi desain Python menekankan keterbacaan kode dengan penggunaan spasi putih yang signifikan. Konstruksi bahasanya dan pendekatan berorientasi objek bertujuan untuk membantu pemrogram menulis kode yang jelas dan logis untuk proyek skala kecil dan besar." def create_queries(para): input_ids = tokenizer.encode(para, return_tensors='pt') with torch.no_grad(): # Here we use top_k / top_k random sampling. It generates more diverse queries, but of lower quality sampling_outputs = model.generate( input_ids=input_ids, max_length=64, do_sample=True, top_p=0.95, top_k=10, num_return_sequences=5 ) # Here we use Beam-search. It generates better quality queries, but with less diversity beam_outputs = model.generate( input_ids=input_ids, max_length=64, num_beams=5, no_repeat_ngram_size=2, num_return_sequences=5, early_stopping=True ) print("Paragraph:") print(para) print("\nBeam Outputs:") for i in range(len(beam_outputs)): query = tokenizer.decode(beam_outputs[i], skip_special_tokens=True) print(f'{i + 1}: {query}') print("\nSampling Outputs:") for i in range(len(sampling_outputs)): query = tokenizer.decode(sampling_outputs[i], skip_special_tokens=True) print(f'{i + 1}: {query}') create_queries(text) ``` Note: `model.generate()` is non-deterministic for top_k/top_n sampling. It produces different queries each time you run it. ## Training This model fine-tuned [google/mt5-base](https://huggingface.co/google/mt5-base) for 66k training steps (4 epochs on the 500k training pairs from MS MARCO). For the training script, see the `train_script.py` in this repository. The input-text was truncated to 320 word pieces. Output text was generated up to 64 word pieces. This model was trained on a (query, passage) from the [mMARCO dataset](https://github.com/unicamp-dl/mMARCO).
huggan/pggan-celebahq-1024	huggan	2022-04-29T11:58:41Z	0	0	null	[ "pytorch", "gan", "pggan", "huggan", "unconditional-image-generation", "license:apache-2.0", "region:us" ]	unconditional-image-generation	2022-04-17T19:15:25Z	--- license: apache-2.0 tags: - gan - pggan - huggan - unconditional-image-generation --- The model provided is a PGGAN generator trained on the celebahq dataset with a resolution of 1024px. It is uploaded as part of porting this project: https://github.com/genforce/sefa to hugginface spaces.
doc2query/msmarco-hindi-mt5-base-v1	doc2query	2022-04-29T11:56:03Z	5	1	transformers	[ "transformers", "pytorch", "mt5", "text2text-generation", "hi", "dataset:unicamp-dl/mmarco", "arxiv:1904.08375", "arxiv:2104.08663", "arxiv:2112.07577", "license:apache-2.0", "autotrain_compatible", "endpoints_compatible", "region:us" ]	text2text-generation	2022-04-29T11:55:47Z	--- language: hi datasets: - unicamp-dl/mmarco widget: - text: "पाइथन एक सामान्य कार्यों के लिए उपयुक्त, उच्च स्तरीय प्रोग्रामिंग भाषा (General Purpose and High Level Programming language), इन्टरैक्टिव, ऑब्जेक्ट ओरिएन्टेड, स्क्रिप्टिंग भाषा है। इस भाषा को इस तरह से डिजाइन किया गया है ताकि इसमें लिखे गए कोड आसानी से पढ़े और समझे जा सकें।" license: apache-2.0 --- # doc2query/msmarco-hindi-mt5-base-v1 This is a [doc2query](https://arxiv.org/abs/1904.08375) model based on mT5 (also known as [docT5query](https://cs.uwaterloo.ca/~jimmylin/publications/Nogueira_Lin_2019_docTTTTTquery-v2.pdf)). It can be used for: - Document expansion: You generate for your paragraphs 20-40 queries and index the paragraphs and the generates queries in a standard BM25 index like Elasticsearch, OpenSearch, or Lucene. The generated queries help to close the lexical gap of lexical search, as the generate queries contain synonyms. Further, it re-weights words giving important words a higher weight even if they appear seldomn in a paragraph. In our [BEIR](https://arxiv.org/abs/2104.08663) paper we showed that BM25+docT5query is a powerful search engine. In the [BEIR repository](https://github.com/beir-cellar/beir) we have an example how to use docT5query with Pyserini. - Domain Specific Training Data Generation: It can be used to generate training data to learn an embedding model. In our [GPL-Paper](https://arxiv.org/abs/2112.07577) / [GPL Example on SBERT.net](https://www.sbert.net/examples/domain_adaptation/README.html#gpl-generative-pseudo-labeling) we have an example how to use the model to generate (query, text) pairs for a given collection of unlabeled texts. These pairs can then be used to train powerful dense embedding models. ## Usage ```python from transformers import AutoTokenizer, AutoModelForSeq2SeqLM import torch model_name = 'doc2query/msmarco-hindi-mt5-base-v1' tokenizer = AutoTokenizer.from_pretrained(model_name) model = AutoModelForSeq2SeqLM.from_pretrained(model_name) text = "पाइथन एक सामान्य कार्यों के लिए उपयुक्त, उच्च स्तरीय प्रोग्रामिंग भाषा (General Purpose and High Level Programming language), इन्टरैक्टिव, ऑब्जेक्ट ओरिएन्टेड, स्क्रिप्टिंग भाषा है। इस भाषा को इस तरह से डिजाइन किया गया है ताकि इसमें लिखे गए कोड आसानी से पढ़े और समझे जा सकें।" def create_queries(para): input_ids = tokenizer.encode(para, return_tensors='pt') with torch.no_grad(): # Here we use top_k / top_k random sampling. It generates more diverse queries, but of lower quality sampling_outputs = model.generate( input_ids=input_ids, max_length=64, do_sample=True, top_p=0.95, top_k=10, num_return_sequences=5 ) # Here we use Beam-search. It generates better quality queries, but with less diversity beam_outputs = model.generate( input_ids=input_ids, max_length=64, num_beams=5, no_repeat_ngram_size=2, num_return_sequences=5, early_stopping=True ) print("Paragraph:") print(para) print("\nBeam Outputs:") for i in range(len(beam_outputs)): query = tokenizer.decode(beam_outputs[i], skip_special_tokens=True) print(f'{i + 1}: {query}') print("\nSampling Outputs:") for i in range(len(sampling_outputs)): query = tokenizer.decode(sampling_outputs[i], skip_special_tokens=True) print(f'{i + 1}: {query}') create_queries(text) ``` Note: `model.generate()` is non-deterministic for top_k/top_n sampling. It produces different queries each time you run it. ## Training This model fine-tuned [google/mt5-base](https://huggingface.co/google/mt5-base) for 66k training steps (4 epochs on the 500k training pairs from MS MARCO). For the training script, see the `train_script.py` in this repository. The input-text was truncated to 320 word pieces. Output text was generated up to 64 word pieces. This model was trained on a (query, passage) from the [mMARCO dataset](https://github.com/unicamp-dl/mMARCO).
norefly/opus-mt-ko-en-finetuned-ko-to-en3	norefly	2022-04-29T11:48:26Z	3	0	transformers	[ "transformers", "pytorch", "tensorboard", "marian", "text2text-generation", "generated_from_trainer", "license:apache-2.0", "autotrain_compatible", "endpoints_compatible", "region:us" ]	text2text-generation	2022-04-29T04:28:20Z	--- license: apache-2.0 tags: - generated_from_trainer metrics: - bleu model-index: - name: opus-mt-ko-en-finetuned-ko-to-en3 results: [] --- <!-- This model card has been generated automatically according to the information the Trainer had access to. You should probably proofread and complete it, then remove this comment. --> # opus-mt-ko-en-finetuned-ko-to-en3 This model is a fine-tuned version of [Helsinki-NLP/opus-mt-ko-en](https://huggingface.co/Helsinki-NLP/opus-mt-ko-en) on the None dataset. It achieves the following results on the evaluation set: - Loss: 2.1864 - Bleu: 0.7037 - Gen Len: 11.0 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 0.001 - train_batch_size: 8 - eval_batch_size: 8 - seed: 42 - gradient_accumulation_steps: 256 - total_train_batch_size: 2048 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - num_epochs: 5 - mixed_precision_training: Native AMP ### Training results \| Training Loss \| Epoch \| Step \| Validation Loss \| Bleu \| Gen Len \| \|:-------------:\|:-----:\|:----:\|:---------------:\|:------:\|:-------:\| \| No log \| 0.99 \| 119 \| 4.4541 \| 0.0 \| 5.0 \| \| No log \| 1.99 \| 238 \| 2.4214 \| 0.3414 \| 16.0 \| \| No log \| 2.99 \| 357 \| 2.2158 \| 0.3212 \| 15.0 \| \| No log \| 3.99 \| 476 \| 2.1737 \| 0.3283 \| 12.0 \| \| 3.2958 \| 4.99 \| 595 \| 2.1864 \| 0.7037 \| 11.0 \| ### Framework versions - Transformers 4.18.0 - Pytorch 1.11.0+cu113 - Datasets 2.1.0 - Tokenizers 0.12.1
doc2query/msmarco-arabic-mt5-base-v1	doc2query	2022-04-29T11:42:59Z	4	1	transformers	[ "transformers", "pytorch", "mt5", "text2text-generation", "ar", "dataset:unicamp-dl/mmarco", "arxiv:1904.08375", "arxiv:2104.08663", "arxiv:2112.07577", "license:apache-2.0", "autotrain_compatible", "endpoints_compatible", "region:us" ]	text2text-generation	2022-04-29T11:42:40Z	--- language: ar datasets: - unicamp-dl/mmarco widget: - text: "بايثون (بالإنجليزية: Python)‏ هي لغة برمجة، عالية المستوى سهلة التعلم مفتوحة المصدر قابلة للتوسيع، تعتمد أسلوب البرمجة الكائنية (OOP). لغة بايثون هي لغة مُفسَّرة، ومُتعدِدة الاستخدامات، وتستخدم بشكل واسع في العديد من المجالات، كبناء البرامج المستقلة باستخدام الواجهات الرسومية وفي تطبيقات الويب، ويمكن استخدامها كلغة برمجة نصية للتحكم في أداء العديد من البرمجيات مثل بلندر. بشكل عام، يمكن استخدام بايثون لعمل البرامج البسيطة للمبتدئين، ولإنجاز المشاريع الضخمة في الوقت نفسه. غالباً ما يُنصح المبتدؤون في ميدان البرمجة بتعلم هذه اللغة لأنها من بين أسرع اللغات البرمجية تعلماً." license: apache-2.0 --- # doc2query/msmarco-arabic-mt5-base-v1 This is a [doc2query](https://arxiv.org/abs/1904.08375) model based on mT5 (also known as [docT5query](https://cs.uwaterloo.ca/~jimmylin/publications/Nogueira_Lin_2019_docTTTTTquery-v2.pdf)). It can be used for: - Document expansion: You generate for your paragraphs 20-40 queries and index the paragraphs and the generates queries in a standard BM25 index like Elasticsearch, OpenSearch, or Lucene. The generated queries help to close the lexical gap of lexical search, as the generate queries contain synonyms. Further, it re-weights words giving important words a higher weight even if they appear seldomn in a paragraph. In our [BEIR](https://arxiv.org/abs/2104.08663) paper we showed that BM25+docT5query is a powerful search engine. In the [BEIR repository](https://github.com/beir-cellar/beir) we have an example how to use docT5query with Pyserini. - Domain Specific Training Data Generation: It can be used to generate training data to learn an embedding model. In our [GPL-Paper](https://arxiv.org/abs/2112.07577) / [GPL Example on SBERT.net](https://www.sbert.net/examples/domain_adaptation/README.html#gpl-generative-pseudo-labeling) we have an example how to use the model to generate (query, text) pairs for a given collection of unlabeled texts. These pairs can then be used to train powerful dense embedding models. ## Usage ```python from transformers import AutoTokenizer, AutoModelForSeq2SeqLM import torch model_name = 'doc2query/msmarco-arabic-mt5-base-v1' tokenizer = AutoTokenizer.from_pretrained(model_name) model = AutoModelForSeq2SeqLM.from_pretrained(model_name) text = "بايثون (بالإنجليزية: Python)‏ هي لغة برمجة، عالية المستوى سهلة التعلم مفتوحة المصدر قابلة للتوسيع، تعتمد أسلوب البرمجة الكائنية (OOP). لغة بايثون هي لغة مُفسَّرة، ومُتعدِدة الاستخدامات، وتستخدم بشكل واسع في العديد من المجالات، كبناء البرامج المستقلة باستخدام الواجهات الرسومية وفي تطبيقات الويب، ويمكن استخدامها كلغة برمجة نصية للتحكم في أداء العديد من البرمجيات مثل بلندر. بشكل عام، يمكن استخدام بايثون لعمل البرامج البسيطة للمبتدئين، ولإنجاز المشاريع الضخمة في الوقت نفسه. غالباً ما يُنصح المبتدؤون في ميدان البرمجة بتعلم هذه اللغة لأنها من بين أسرع اللغات البرمجية تعلماً." def create_queries(para): input_ids = tokenizer.encode(para, return_tensors='pt') with torch.no_grad(): # Here we use top_k / top_k random sampling. It generates more diverse queries, but of lower quality sampling_outputs = model.generate( input_ids=input_ids, max_length=64, do_sample=True, top_p=0.95, top_k=10, num_return_sequences=5 ) # Here we use Beam-search. It generates better quality queries, but with less diversity beam_outputs = model.generate( input_ids=input_ids, max_length=64, num_beams=5, no_repeat_ngram_size=2, num_return_sequences=5, early_stopping=True ) print("Paragraph:") print(para) print("\nBeam Outputs:") for i in range(len(beam_outputs)): query = tokenizer.decode(beam_outputs[i], skip_special_tokens=True) print(f'{i + 1}: {query}') print("\nSampling Outputs:") for i in range(len(sampling_outputs)): query = tokenizer.decode(sampling_outputs[i], skip_special_tokens=True) print(f'{i + 1}: {query}') create_queries(text) ``` Note: `model.generate()` is non-deterministic for top_k/top_n sampling. It produces different queries each time you run it. ## Training This model fine-tuned [google/mt5-base](https://huggingface.co/google/mt5-base) for 66k training steps (4 epochs on the 500k training pairs from MS MARCO). For the training script, see the `train_script.py` in this repository. The input-text was truncated to 320 word pieces. Output text was generated up to 64 word pieces. This model was trained on a (query, passage) from the [mMARCO dataset](https://github.com/unicamp-dl/mMARCO).
ZZ99/deberta-v3-large-tapt	ZZ99	2022-04-29T09:24:11Z	6	0	transformers	[ "transformers", "pytorch", "deberta-v2", "fill-mask", "generated_from_trainer", "autotrain_compatible", "endpoints_compatible", "region:us" ]	fill-mask	2022-04-26T09:27:01Z	--- tags: - generated_from_trainer metrics: - accuracy model-index: - name: test-mlm results: [] --- <!-- This model card has been generated automatically according to the information the Trainer had access to. You should probably proofread and complete it, then remove this comment. --> # test-mlm This model is a fine-tuned version of [/root/autodl-tmp/nbme/tmp/test-mlm/deberta-v3-large-tapt](https://huggingface.co//root/autodl-tmp/nbme/tmp/test-mlm/deberta-v3-large-tapt) on an unknown dataset. It achieves the following results on the evaluation set: - Loss: 1.3251 - Accuracy: 0.7285 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 1e-05 - train_batch_size: 4 - eval_batch_size: 8 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - num_epochs: 3.0 ### Training results ### Framework versions - Transformers 4.19.0.dev0 - Pytorch 1.11.0 - Datasets 2.1.0 - Tokenizers 0.12.1
doc2query/msmarco-german-mt5-base-v1	doc2query	2022-04-29T09:03:18Z	20	6	transformers	[ "transformers", "pytorch", "mt5", "text2text-generation", "de", "dataset:unicamp-dl/mmarco", "arxiv:1904.08375", "arxiv:2104.08663", "arxiv:2112.07577", "license:apache-2.0", "autotrain_compatible", "endpoints_compatible", "region:us" ]	text2text-generation	2022-04-29T08:49:21Z	--- language: de datasets: - unicamp-dl/mmarco widget: - text: "Python ist eine universelle, üblicherweise interpretierte, höhere Programmiersprache. Sie hat den Anspruch, einen gut lesbaren, knappen Programmierstil zu fördern. So werden beispielsweise Blöcke nicht durch geschweifte Klammern, sondern durch Einrückungen strukturiert." license: apache-2.0 --- # doc2query/msmarco-german-mt5-base-v1 This is a [doc2query](https://arxiv.org/abs/1904.08375) model based on mT5 (also known as [docT5query](https://cs.uwaterloo.ca/~jimmylin/publications/Nogueira_Lin_2019_docTTTTTquery-v2.pdf)). It can be used for: - Document expansion: You generate for your paragraphs 20-40 queries and index the paragraphs and the generates queries in a standard BM25 index like Elasticsearch, OpenSearch, or Lucene. The generated queries help to close the lexical gap of lexical search, as the generate queries contain synonyms. Further, it re-weights words giving important words a higher weight even if they appear seldomn in a paragraph. In our [BEIR](https://arxiv.org/abs/2104.08663) paper we showed that BM25+docT5query is a powerful search engine. In the [BEIR repository](https://github.com/beir-cellar/beir) we have an example how to use docT5query with Pyserini. - Domain Specific Training Data Generation: It can be used to generate training data to learn an embedding model. In our [GPL-Paper](https://arxiv.org/abs/2112.07577) / [GPL Example on SBERT.net](https://www.sbert.net/examples/domain_adaptation/README.html#gpl-generative-pseudo-labeling) we have an example how to use the model to generate (query, text) pairs for a given collection of unlabeled texts. These pairs can then be used to train powerful dense embedding models. ## Usage ```python from transformers import AutoTokenizer, AutoModelForSeq2SeqLM import torch model_name = 'doc2query/msmarco-german-mt5-base-v1' tokenizer = AutoTokenizer.from_pretrained(model_name) model = AutoModelForSeq2SeqLM.from_pretrained(model_name) text = "Python ist eine universelle, üblicherweise interpretierte, höhere Programmiersprache. Sie hat den Anspruch, einen gut lesbaren, knappen Programmierstil zu fördern. So werden beispielsweise Blöcke nicht durch geschweifte Klammern, sondern durch Einrückungen strukturiert." def create_queries(para): input_ids = tokenizer.encode(para, return_tensors='pt') with torch.no_grad(): # Here we use top_k / top_k random sampling. It generates more diverse queries, but of lower quality sampling_outputs = model.generate( input_ids=input_ids, max_length=64, do_sample=True, top_p=0.95, top_k=10, num_return_sequences=5 ) # Here we use Beam-search. It generates better quality queries, but with less diversity beam_outputs = model.generate( input_ids=input_ids, max_length=64, num_beams=5, no_repeat_ngram_size=2, num_return_sequences=5, early_stopping=True ) print("Paragraph:") print(para) print("\nBeam Outputs:") for i in range(len(beam_outputs)): query = tokenizer.decode(beam_outputs[i], skip_special_tokens=True) print(f'{i + 1}: {query}') print("\nSampling Outputs:") for i in range(len(sampling_outputs)): query = tokenizer.decode(sampling_outputs[i], skip_special_tokens=True) print(f'{i + 1}: {query}') create_queries(text) ``` Note: `model.generate()` is non-deterministic for top_k/top_n sampling. It produces different queries each time you run it. ## Training This model fine-tuned [google/mt5-base](https://huggingface.co/google/mt5-base) for 66k training steps (4 epochs on the 500k training pairs from MS MARCO). For the training script, see the `train_script.py` in this repository. The input-text was truncated to 320 word pieces. Output text was generated up to 64 word pieces. This model was trained on a (query, passage) from the [mMARCO dataset](https://github.com/unicamp-dl/mMARCO).
Das282000Prit/fyp-finetuned-brown	Das282000Prit	2022-04-29T06:41:10Z	3	0	transformers	[ "transformers", "tf", "bert", "fill-mask", "generated_from_keras_callback", "license:apache-2.0", "autotrain_compatible", "endpoints_compatible", "region:us" ]	fill-mask	2022-04-29T06:15:15Z	--- license: apache-2.0 tags: - generated_from_keras_callback model-index: - name: Das282000Prit/fyp-finetuned-brown results: [] --- <!-- This model card has been generated automatically according to the information Keras had access to. You should probably proofread and complete it, then remove this comment. --> # Das282000Prit/fyp-finetuned-brown This model is a fine-tuned version of [bert-base-uncased](https://huggingface.co/bert-base-uncased) on an unknown dataset. It achieves the following results on the evaluation set: - Train Loss: 3.5777 - Validation Loss: 3.0737 - Epoch: 0 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - optimizer: {'name': 'AdamWeightDecay', 'learning_rate': {'class_name': 'WarmUp', 'config': {'initial_learning_rate': 2e-05, 'decay_schedule_fn': {'class_name': 'PolynomialDecay', 'config': {'initial_learning_rate': 2e-05, 'decay_steps': -844, 'end_learning_rate': 0.0, 'power': 1.0, 'cycle': False, 'name': None}, '__passive_serialization__': True}, 'warmup_steps': 1000, 'power': 1.0, 'name': None}}, 'decay': 0.0, 'beta_1': 0.9, 'beta_2': 0.999, 'epsilon': 1e-08, 'amsgrad': False, 'weight_decay_rate': 0.01} - training_precision: mixed_float16 ### Training results \| Train Loss \| Validation Loss \| Epoch \| \|:----------:\|:---------------:\|:-----:\| \| 3.5777 \| 3.0737 \| 0 \| ### Framework versions - Transformers 4.18.0 - TensorFlow 2.8.0 - Datasets 2.1.0 - Tokenizers 0.12.1
NLPC-UOM/SinBERT-large	NLPC-UOM	2022-04-29T05:05:04Z	196	5	transformers	[ "transformers", "pytorch", "roberta", "fill-mask", "si", "license:mit", "autotrain_compatible", "endpoints_compatible", "region:us" ]	fill-mask	2022-03-02T23:29:04Z	--- language: - si license: - mit --- This is SinBERT-large model. SinBERT models are pretrained on a large Sinhala monolingual corpus (sin-cc-15M) using RoBERTa. If you use this model, please cite BERTifying Sinhala - A Comprehensive Analysis of Pre-trained Language Models for Sinhala Text Classification, LREC 2022
NLPC-UOM/SinBERT-small	NLPC-UOM	2022-04-29T05:04:13Z	72	4	transformers	[ "transformers", "pytorch", "roberta", "fill-mask", "si", "license:mit", "autotrain_compatible", "endpoints_compatible", "region:us" ]	fill-mask	2022-03-02T23:29:04Z	--- license: mit language: - si --- This is SinBERT-small model. SinBERT models are pretrained on a large Sinhala monolingual corpus (sin-cc-15M) using RoBERTa. If you use this model, please cite BERTifying Sinhala - A Comprehensive Analysis of Pre-trained Language Models for Sinhala Text Classification, LREC 2022
bkh6722/xlsr-vorarlbergerisch	bkh6722	2022-04-29T04:45:04Z	4	0	transformers	[ "transformers", "pytorch", "tensorboard", "wav2vec2", "automatic-speech-recognition", "generated_from_trainer", "license:apache-2.0", "endpoints_compatible", "region:us" ]	automatic-speech-recognition	2022-04-29T02:50:28Z	--- license: apache-2.0 tags: - generated_from_trainer model-index: name: wav2vec2-xlsr-vorarlbergerisch --- <!-- This model card has been generated automatically according to the information the Trainer had access to. You should probably proofread and complete it, then remove this comment. --> # wav2vec2-xlsr-vorarlbergerisch This model is a fine-tuned version of [jonatasgrosman/wav2vec2-large-xlsr-53-german](https://huggingface.co/jonatasgrosman/wav2vec2-large-xlsr-53-german) on an unknown dataset. It achieves the following results on the evaluation set: - Loss: 1.3193 - Wer: 0.3235 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 0.0003 - train_batch_size: 16 - eval_batch_size: 8 - seed: 42 - gradient_accumulation_steps: 2 - total_train_batch_size: 32 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - lr_scheduler_warmup_steps: 500 - num_epochs: 62 - mixed_precision_training: Native AMP ### Training results \| Training Loss \| Epoch \| Step \| Validation Loss \| Wer \| \|:-------------:\|:-----:\|:----:\|:---------------:\|:------:\| \| 15.6717 \| 3.83 \| 100 \| 3.0247 \| 1.0 \| \| 2.485 \| 7.68 \| 200 \| 1.5937 \| 0.9046 \| \| 0.784 \| 11.53 \| 300 \| 1.2664 \| 0.5 \| \| 0.3689 \| 15.38 \| 400 \| 1.2046 \| 0.4696 \| \| 0.2618 \| 19.23 \| 500 \| 1.1289 \| 0.4155 \| \| 0.2088 \| 23.08 \| 600 \| 0.9339 \| 0.3623 \| \| 0.1388 \| 26.91 \| 700 \| 1.1448 \| 0.3573 \| \| 0.1042 \| 30.75 \| 800 \| 1.1411 \| 0.3606 \| \| 0.0784 \| 34.6 \| 900 \| 1.2046 \| 0.3547 \| \| 0.0607 \| 38.45 \| 1000 \| 1.2243 \| 0.3488 \| \| 0.0459 \| 42.3 \| 1100 \| 1.2387 \| 0.3226 \| \| 0.0273 \| 46.15 \| 1200 \| 1.2123 \| 0.3387 \| \| 0.0195 \| 49.98 \| 1300 \| 1.2232 \| 0.3345 \| \| 0.0188 \| 53.83 \| 1400 \| 1.2656 \| 0.3235 \| \| 0.0132 \| 57.68 \| 1500 \| 1.3377 \| 0.3285 \| \| 0.0089 \| 61.53 \| 1600 \| 1.3193 \| 0.3235 \| ### Framework versions - Transformers 4.11.3 - Pytorch 1.10.0+cu113 - Datasets 1.18.3 - Tokenizers 0.10.3
kimhieu/distilbert-base-uncased-finetuned-cola	kimhieu	2022-04-29T03:44:47Z	5	0	transformers	[ "transformers", "tf", "tensorboard", "distilbert", "text-classification", "generated_from_keras_callback", "license:apache-2.0", "autotrain_compatible", "endpoints_compatible", "region:us" ]	text-classification	2022-04-29T02:39:26Z	--- license: apache-2.0 tags: - generated_from_keras_callback model-index: - name: kimhieu/distilbert-base-uncased-finetuned-cola results: [] --- <!-- This model card has been generated automatically according to the information Keras had access to. You should probably proofread and complete it, then remove this comment. --> # kimhieu/distilbert-base-uncased-finetuned-cola This model is a fine-tuned version of [distilbert-base-uncased](https://huggingface.co/distilbert-base-uncased) on an unknown dataset. It achieves the following results on the evaluation set: - Train Loss: 0.1828 - Validation Loss: 0.5520 - Train Matthews Correlation: 0.5286 - Epoch: 2 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - optimizer: {'name': 'Adam', 'learning_rate': {'class_name': 'PolynomialDecay', 'config': {'initial_learning_rate': 2e-05, 'decay_steps': 2670, 'end_learning_rate': 0.0, 'power': 1.0, 'cycle': False, 'name': None}}, 'decay': 0.0, 'beta_1': 0.9, 'beta_2': 0.999, 'epsilon': 1e-08, 'amsgrad': False} - training_precision: float32 ### Training results \| Train Loss \| Validation Loss \| Train Matthews Correlation \| Epoch \| \|:----------:\|:---------------:\|:--------------------------:\|:-----:\| \| 0.5184 \| 0.4675 \| 0.4484 \| 0 \| \| 0.3164 \| 0.4646 \| 0.4963 \| 1 \| \| 0.1828 \| 0.5520 \| 0.5286 \| 2 \| ### Framework versions - Transformers 4.18.0 - TensorFlow 2.8.0 - Datasets 2.1.0 - Tokenizers 0.12.1
chv5/t5-small-shuffled_take3-small	chv5	2022-04-29T03:26:41Z	3	0	transformers	[ "transformers", "pytorch", "tensorboard", "t5", "text2text-generation", "generated_from_trainer", "dataset:xsum", "license:apache-2.0", "model-index", "autotrain_compatible", "text-generation-inference", "endpoints_compatible", "region:us" ]	text2text-generation	2022-04-28T18:06:27Z	--- license: apache-2.0 tags: - generated_from_trainer datasets: - xsum metrics: - rouge model-index: - name: t5-small-shuffled_take3-small results: - task: name: Sequence-to-sequence Language Modeling type: text2text-generation dataset: name: xsum type: xsum args: default metrics: - name: Rouge1 type: rouge value: 11.883 --- <!-- This model card has been generated automatically according to the information the Trainer had access to. You should probably proofread and complete it, then remove this comment. --> # t5-small-shuffled_take3-small This model is a fine-tuned version of [t5-small](https://huggingface.co/t5-small) on the xsum dataset. It achieves the following results on the evaluation set: - Loss: 0.4505 - Rouge1: 11.883 - Rouge2: 9.4784 - Rougel: 10.9978 - Rougelsum: 11.5961 - Gen Len: 18.9834 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 2e-05 - train_batch_size: 6 - eval_batch_size: 6 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - num_epochs: 1 - mixed_precision_training: Native AMP ### Training results \| Training Loss \| Epoch \| Step \| Validation Loss \| Rouge1 \| Rouge2 \| Rougel \| Rougelsum \| Gen Len \| \|:-------------:\|:-----:\|:-----:\|:---------------:\|:------:\|:------:\|:-------:\|:---------:\|:-------:\| \| 0.5205 \| 1.0 \| 34008 \| 0.4505 \| 11.883 \| 9.4784 \| 10.9978 \| 11.5961 \| 18.9834 \| ### Framework versions - Transformers 4.18.0 - Pytorch 1.11.0+cu113 - Datasets 2.1.0 - Tokenizers 0.12.1
bkh6722/wav2vec2-vorarlbergerisch	bkh6722	2022-04-29T02:50:23Z	3	0	transformers	[ "transformers", "pytorch", "tensorboard", "wav2vec2", "automatic-speech-recognition", "generated_from_trainer", "license:apache-2.0", "endpoints_compatible", "region:us" ]	automatic-speech-recognition	2022-04-28T22:14:18Z	--- license: apache-2.0 tags: - generated_from_trainer model-index: name: wav2vec2-vorarlbergerisch --- <!-- This model card has been generated automatically according to the information the Trainer had access to. You should probably proofread and complete it, then remove this comment. --> # wav2vec2-vorarlbergerisch This model is a fine-tuned version of [facebook/wav2vec2-base-960h](https://huggingface.co/facebook/wav2vec2-base-960h) on an unknown dataset. It achieves the following results on the evaluation set: - Loss: 1.9241 - Wer: 0.4358 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 0.0003 - train_batch_size: 16 - eval_batch_size: 8 - seed: 42 - gradient_accumulation_steps: 2 - total_train_batch_size: 32 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - lr_scheduler_warmup_steps: 500 - num_epochs: 62 - mixed_precision_training: Native AMP ### Training results \| Training Loss \| Epoch \| Step \| Validation Loss \| Wer \| \|:-------------:\|:-----:\|:----:\|:---------------:\|:------:\| \| 12.6837 \| 3.83 \| 100 \| 3.7188 \| 1.0 \| \| 3.33 \| 7.68 \| 200 \| 3.0620 \| 1.0 \| \| 2.9508 \| 11.53 \| 300 \| 2.5915 \| 1.0101 \| \| 1.8954 \| 15.38 \| 400 \| 1.6930 \| 0.8243 \| \| 1.231 \| 19.23 \| 500 \| 1.7179 \| 0.7551 \| \| 0.9862 \| 23.08 \| 600 \| 1.5237 \| 0.6529 \| \| 0.7353 \| 26.91 \| 700 \| 1.5119 \| 0.5921 \| \| 0.5368 \| 30.75 \| 800 \| 1.5011 \| 0.5574 \| \| 0.4448 \| 34.6 \| 900 \| 1.5334 \| 0.5363 \| \| 0.3278 \| 38.45 \| 1000 \| 1.7125 \| 0.5144 \| \| 0.2575 \| 42.3 \| 1100 \| 1.6529 \| 0.4958 \| \| 0.1966 \| 46.15 \| 1200 \| 1.7670 \| 0.4848 \| \| 0.1552 \| 49.98 \| 1300 \| 1.7586 \| 0.4620 \| \| 0.1118 \| 53.83 \| 1400 \| 1.7912 \| 0.4417 \| \| 0.0847 \| 57.68 \| 1500 \| 1.8709 \| 0.4443 \| \| 0.0654 \| 61.53 \| 1600 \| 1.9241 \| 0.4358 \| ### Framework versions - Transformers 4.11.3 - Pytorch 1.10.0+cu113 - Datasets 1.18.3 - Tokenizers 0.10.3
Rerare/distilbert-base-uncased-finetuned-cola	Rerare	2022-04-29T02:19:11Z	9	0	transformers	[ "transformers", "pytorch", "tensorboard", "distilbert", "text-classification", "generated_from_trainer", "dataset:glue", "license:apache-2.0", "model-index", "autotrain_compatible", "endpoints_compatible", "region:us" ]	text-classification	2022-04-28T12:36:56Z	--- license: apache-2.0 tags: - generated_from_trainer datasets: - glue metrics: - matthews_correlation model-index: - name: distilbert-base-uncased-finetuned-cola results: - task: name: Text Classification type: text-classification dataset: name: glue type: glue args: cola metrics: - name: Matthews Correlation type: matthews_correlation value: 0.5291140309961344 --- <!-- This model card has been generated automatically according to the information the Trainer had access to. You should probably proofread and complete it, then remove this comment. --> # distilbert-base-uncased-finetuned-cola This model is a fine-tuned version of [distilbert-base-uncased](https://huggingface.co/distilbert-base-uncased) on the glue dataset. It achieves the following results on the evaluation set: - Loss: 0.7643 - Matthews Correlation: 0.5291 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 2e-05 - train_batch_size: 16 - eval_batch_size: 16 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - num_epochs: 5 ### Training results \| Training Loss \| Epoch \| Step \| Validation Loss \| Matthews Correlation \| \|:-------------:\|:-----:\|:----:\|:---------------:\|:--------------------:\| \| 0.5288 \| 1.0 \| 535 \| 0.5111 \| 0.4154 \| \| 0.3546 \| 2.0 \| 1070 \| 0.5285 \| 0.4887 \| \| 0.235 \| 3.0 \| 1605 \| 0.5950 \| 0.5153 \| \| 0.1722 \| 4.0 \| 2140 \| 0.7643 \| 0.5291 \| \| 0.1346 \| 5.0 \| 2675 \| 0.8441 \| 0.5185 \| ### Framework versions - Transformers 4.18.0 - Pytorch 1.11.0+cu113 - Datasets 2.1.0 - Tokenizers 0.12.1
obokkkk/mt5-base	obokkkk	2022-04-29T02:04:16Z	19	0	transformers	[ "transformers", "pytorch", "tensorboard", "mt5", "text2text-generation", "generated_from_trainer", "autotrain_compatible", "endpoints_compatible", "region:us" ]	text2text-generation	2022-04-28T05:42:00Z	--- tags: - generated_from_trainer metrics: - bleu model-index: - name: mt5-base results: [] --- <!-- This model card has been generated automatically according to the information the Trainer had access to. You should probably proofread and complete it, then remove this comment. --> # mt5-base This model was trained from scratch on the None dataset. It achieves the following results on the evaluation set: - Loss: 1.2760 - Bleu: 8.6707 - Gen Len: 16.9319 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 0.001 - train_batch_size: 8 - eval_batch_size: 8 - seed: 42 - gradient_accumulation_steps: 256 - total_train_batch_size: 2048 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - num_epochs: 5 ### Training results \| Training Loss \| Epoch \| Step \| Validation Loss \| Bleu \| Gen Len \| \|:-------------:\|:-----:\|:----:\|:---------------:\|:------:\|:-------:\| \| No log \| 1.0 \| 183 \| 1.4997 \| 6.2141 \| 17.0073 \| \| No log \| 2.0 \| 366 \| 1.3718 \| 7.4647 \| 16.9205 \| \| 1.9408 \| 3.0 \| 549 \| 1.3184 \| 8.1938 \| 16.8962 \| \| 1.9408 \| 4.0 \| 732 \| 1.2857 \| 8.5265 \| 16.9167 \| \| 1.9408 \| 5.0 \| 915 \| 1.2760 \| 8.6707 \| 16.9319 \| ### Framework versions - Transformers 4.18.0 - Pytorch 1.11.0+cu113 - Datasets 2.1.0 - Tokenizers 0.12.1
obokkkk/wav2vec2-base-960h-finetuned_common_voice3	obokkkk	2022-04-29T00:37:29Z	3	0	transformers	[ "transformers", "pytorch", "tensorboard", "wav2vec2", "automatic-speech-recognition", "generated_from_trainer", "license:apache-2.0", "endpoints_compatible", "region:us" ]	automatic-speech-recognition	2022-04-28T05:57:45Z	--- license: apache-2.0 tags: - generated_from_trainer model-index: - name: wav2vec2-base-960h-finetuned_common_voice3 results: [] --- <!-- This model card has been generated automatically according to the information the Trainer had access to. You should probably proofread and complete it, then remove this comment. --> # wav2vec2-base-960h-finetuned_common_voice3 This model is a fine-tuned version of [facebook/wav2vec2-base-960h](https://huggingface.co/facebook/wav2vec2-base-960h) on the None dataset. ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 0.001 - train_batch_size: 16 - eval_batch_size: 8 - seed: 42 - gradient_accumulation_steps: 64 - total_train_batch_size: 1024 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - lr_scheduler_warmup_steps: 1000 - num_epochs: 30 - mixed_precision_training: Native AMP ### Training results ### Framework versions - Transformers 4.18.0 - Pytorch 1.11.0+cu113 - Datasets 2.1.0 - Tokenizers 0.12.1
lilykaw/distilbert-base-uncased-finetuned-stsb	lilykaw	2022-04-28T22:46:26Z	5	0	transformers	[ "transformers", "pytorch", "tensorboard", "distilbert", "text-classification", "generated_from_trainer", "dataset:glue", "license:apache-2.0", "model-index", "autotrain_compatible", "endpoints_compatible", "region:us" ]	text-classification	2022-04-28T21:14:27Z	--- license: apache-2.0 tags: - generated_from_trainer datasets: - glue metrics: - spearmanr model-index: - name: distilbert-base-uncased-finetuned-stsb results: - task: name: Text Classification type: text-classification dataset: name: glue type: glue args: stsb metrics: - name: Spearmanr type: spearmanr value: 0.8651841336703003 --- <!-- This model card has been generated automatically according to the information the Trainer had access to. You should probably proofread and complete it, then remove this comment. --> # distilbert-base-uncased-finetuned-stsb This model is a fine-tuned version of [distilbert-base-uncased](https://huggingface.co/distilbert-base-uncased) on the glue dataset. It achieves the following results on the evaluation set: - Loss: 0.5634 - Pearson: 0.8680 - Spearmanr: 0.8652 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 2e-05 - train_batch_size: 16 - eval_batch_size: 16 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - num_epochs: 5 ### Training results \| Training Loss \| Epoch \| Step \| Validation Loss \| Pearson \| Spearmanr \| \|:-------------:\|:-----:\|:----:\|:---------------:\|:-------:\|:---------:\| \| No log \| 1.0 \| 360 \| 0.6646 \| 0.8516 \| 0.8494 \| \| 1.0238 \| 2.0 \| 720 \| 0.5617 \| 0.8666 \| 0.8637 \| \| 0.3952 \| 3.0 \| 1080 \| 0.6533 \| 0.8649 \| 0.8646 \| \| 0.3952 \| 4.0 \| 1440 \| 0.5889 \| 0.8651 \| 0.8625 \| \| 0.2488 \| 5.0 \| 1800 \| 0.5634 \| 0.8680 \| 0.8652 \| ### Framework versions - Transformers 4.18.0 - Pytorch 1.11.0+cu113 - Datasets 2.1.0 - Tokenizers 0.12.1
AbhiNaiky/finetuning-sentiment-model-3000-samples	AbhiNaiky	2022-04-28T22:34:39Z	5	0	transformers	[ "transformers", "pytorch", "tensorboard", "distilbert", "text-classification", "generated_from_trainer", "dataset:imdb", "license:apache-2.0", "model-index", "autotrain_compatible", "endpoints_compatible", "region:us" ]	text-classification	2022-04-28T22:16:05Z	--- license: apache-2.0 tags: - generated_from_trainer datasets: - imdb metrics: - accuracy - f1 model-index: - name: finetuning-sentiment-model-3000-samples results: - task: name: Text Classification type: text-classification dataset: name: imdb type: imdb args: plain_text metrics: - name: Accuracy type: accuracy value: 0.8733333333333333 - name: F1 type: f1 value: 0.875 --- <!-- This model card has been generated automatically according to the information the Trainer had access to. You should probably proofread and complete it, then remove this comment. --> # finetuning-sentiment-model-3000-samples This model is a fine-tuned version of [distilbert-base-uncased](https://huggingface.co/distilbert-base-uncased) on the imdb dataset. It achieves the following results on the evaluation set: - Loss: 0.3170 - Accuracy: 0.8733 - F1: 0.875 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 2e-05 - train_batch_size: 16 - eval_batch_size: 16 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - num_epochs: 2 ### Training results ### Framework versions - Transformers 4.18.0 - Pytorch 1.11.0+cu113 - Datasets 2.1.0 - Tokenizers 0.12.1
Sathira/autotrain-mbtiNlp-798824628	Sathira	2022-04-28T22:09:14Z	34	2	transformers	[ "transformers", "pytorch", "distilbert", "text-classification", "autotrain", "en", "dataset:Sathira/autotrain-data-mbtiNlp", "co2_eq_emissions", "autotrain_compatible", "endpoints_compatible", "region:us" ]	text-classification	2022-04-28T21:01:33Z	--- tags: autotrain language: en widget: - text: "I love AutoTrain 🤗" datasets: - Sathira/autotrain-data-mbtiNlp co2_eq_emissions: 121.67185089502216 --- # Model Trained Using AutoTrain - Problem type: Multi-class Classification - Model ID: 798824628 - CO2 Emissions (in grams): 121.67185089502216 ## Validation Metrics - Loss: 0.5046824812889099 - Accuracy: 0.8472124039775673 - Macro F1: 0.7812978033330673 - Micro F1: 0.8472124039775673 - Weighted F1: 0.8464983956259307 - Macro Precision: 0.812208631055716 - Micro Precision: 0.8472124039775673 - Weighted Precision: 0.8478968364150775 - Macro Recall: 0.7593223884993787 - Micro Recall: 0.8472124039775673 - Weighted Recall: 0.8472124039775673 ## Usage You can use cURL to access this model: ``` $ curl -X POST -H "Authorization: Bearer YOUR_API_KEY" -H "Content-Type: application/json" -d '{"inputs": "I love AutoTrain"}' https://api-inference.huggingface.co/models/Sathira/autotrain-mbtiNlp-798824628 ``` Or Python API: ``` from transformers import AutoModelForSequenceClassification, AutoTokenizer model = AutoModelForSequenceClassification.from_pretrained("Sathira/autotrain-mbtiNlp-798824628", use_auth_token=True) tokenizer = AutoTokenizer.from_pretrained("Sathira/autotrain-mbtiNlp-798824628", use_auth_token=True) inputs = tokenizer("I love AutoTrain", return_tensors="pt") outputs = model(**inputs) ```
dannytkn/bert-finetuned-squad	dannytkn	2022-04-28T20:12:13Z	6	0	transformers	[ "transformers", "pytorch", "bert", "question-answering", "generated_from_trainer", "dataset:squad", "license:apache-2.0", "endpoints_compatible", "region:us" ]	question-answering	2022-04-27T09:17:34Z	--- license: apache-2.0 tags: - generated_from_trainer datasets: - squad model-index: - name: bert-finetuned-squad results: [] --- <!-- This model card has been generated automatically according to the information the Trainer had access to. You should probably proofread and complete it, then remove this comment. --> # bert-finetuned-squad This model is a fine-tuned version of [bert-base-cased](https://huggingface.co/bert-base-cased) on the squad dataset. ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 2e-05 - train_batch_size: 8 - eval_batch_size: 8 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - num_epochs: 3 - mixed_precision_training: Native AMP ### Training results ### Framework versions - Transformers 4.16.2 - Pytorch 1.8.2 - Datasets 1.18.3 - Tokenizers 0.10.3
dccuchile/albert-xxlarge-spanish	dccuchile	2022-04-28T19:56:15Z	25	1	transformers	[ "transformers", "pytorch", "tf", "albert", "pretraining", "spanish", "OpenCENIA", "es", "dataset:large_spanish_corpus", "endpoints_compatible", "region:us" ]	null	2022-03-02T23:29:04Z	--- language: - es tags: - albert - spanish - OpenCENIA datasets: - large_spanish_corpus --- # ALBERT XXLarge Spanish This is an [ALBERT](https://github.com/google-research/albert) model trained on a [big spanish corpora](https://github.com/josecannete/spanish-corpora). The model was trained on a single TPU v3-8 with the following hyperparameters and steps/time: - LR: 0.0003125 - Batch Size: 128 - Warmup ratio: 0.00078125 - Warmup steps: 3125 - Goal steps: 4000000 - Total steps: 1650000 - Total training time (aprox): 70.7 days. ## Training loss ![https://drive.google.com/uc?export=view&id=1a9MHsk-QwBuCMtyDyRvZ5mv9Mzl2dWCn](https://drive.google.com/uc?export=view&id=1a9MHsk-QwBuCMtyDyRvZ5mv9Mzl2dWCn)
dccuchile/albert-large-spanish	dccuchile	2022-04-28T19:55:20Z	17	0	transformers	[ "transformers", "pytorch", "tf", "albert", "pretraining", "spanish", "OpenCENIA", "es", "dataset:large_spanish_corpus", "endpoints_compatible", "region:us" ]	null	2022-03-02T23:29:04Z	--- language: - es tags: - albert - spanish - OpenCENIA datasets: - large_spanish_corpus --- # ALBERT Large Spanish This is an [ALBERT](https://github.com/google-research/albert) model trained on a [big spanish corpora](https://github.com/josecannete/spanish-corpora). The model was trained on a single TPU v3-8 with the following hyperparameters and steps/time: - LR: 0.000625 - Batch Size: 512 - Warmup ratio: 0.003125 - Warmup steps: 12500 - Goal steps: 4000000 - Total steps: 1450000 - Total training time (aprox): 42 days. ## Training loss ![https://drive.google.com/uc?export=view&id=10EiI0Yge3U3CnGrqoMs1yJY020pPz_Io](https://drive.google.com/uc?export=view&id=10EiI0Yge3U3CnGrqoMs1yJY020pPz_Io)
dccuchile/albert-base-spanish	dccuchile	2022-04-28T19:55:01Z	246	4	transformers	[ "transformers", "pytorch", "tf", "albert", "pretraining", "spanish", "OpenCENIA", "es", "dataset:large_spanish_corpus", "endpoints_compatible", "region:us" ]	null	2022-03-02T23:29:04Z	--- language: - es tags: - albert - spanish - OpenCENIA datasets: - large_spanish_corpus --- # ALBERT Base Spanish This is an [ALBERT](https://github.com/google-research/albert) model trained on a [big spanish corpora](https://github.com/josecannete/spanish-corpora). The model was trained on a single TPU v3-8 with the following hyperparameters and steps/time: - LR: 0.0008838834765 - Batch Size: 960 - Warmup ratio: 0.00625 - Warmup steps: 53333.33333 - Goal steps: 8533333.333 - Total steps: 3650000 - Total training time (aprox): 70.4 days. ## Training loss ![https://drive.google.com/uc?export=view&id=1IsxcgMwd7Hl-3bSnNl8W9jUrHJeHtZql](https://drive.google.com/uc?export=view&id=1IsxcgMwd7Hl-3bSnNl8W9jUrHJeHtZql)
dccuchile/albert-tiny-spanish	dccuchile	2022-04-28T19:54:10Z	11	3	transformers	[ "transformers", "pytorch", "tf", "albert", "pretraining", "spanish", "OpenCENIA", "es", "dataset:large_spanish_corpus", "endpoints_compatible", "region:us" ]	null	2022-03-02T23:29:04Z	--- language: - es tags: - albert - spanish - OpenCENIA datasets: - large_spanish_corpus --- # ALBERT Tiny Spanish This is an [ALBERT](https://github.com/google-research/albert) model trained on a [big spanish corpora](https://github.com/josecannete/spanish-corpora). The model was trained on a single TPU v3-8 with the following hyperparameters and steps/time: - LR: 0.00125 - Batch Size: 2048 - Warmup ratio: 0.0125 - Warmup steps: 125000 - Goal steps: 10000000 - Total steps: 8300000 - Total training time (aprox): 58.2 days ## Training loss ![https://drive.google.com/uc?export=view&id=1KQc8yWZLKvDLjBtu4IOAgpTx0iLcvX_Q](https://drive.google.com/uc?export=view&id=1KQc8yWZLKvDLjBtu4IOAgpTx0iLcvX_Q)
princeton-nlp/efficient_mlm_m0.50	princeton-nlp	2022-04-28T18:58:09Z	3	0	transformers	[ "transformers", "pytorch", "roberta", "fill-mask", "arxiv:2202.08005", "autotrain_compatible", "region:us" ]	fill-mask	2022-04-28T15:28:20Z	--- inference: false --- This is a model checkpoint for ["Should You Mask 15% in Masked Language Modeling"](https://arxiv.org/abs/2202.08005) [(code)](https://github.com/princeton-nlp/DinkyTrain.git). We use pre layer norm, which is not supported by HuggingFace. To use our model, go to our [github repo](https://github.com/princeton-nlp/DinkyTrain.git), download our code, and import the RoBERTa class from `huggingface/modeling_roberta_prelayernorm.py`. For example, ``` bash from huggingface.modeling_roberta_prelayernorm import RobertaForMaskedLM, RobertaForSequenceClassification ```
princeton-nlp/efficient_mlm_m0.60	princeton-nlp	2022-04-28T18:58:03Z	3	0	transformers	[ "transformers", "pytorch", "roberta", "fill-mask", "arxiv:2202.08005", "autotrain_compatible", "region:us" ]	fill-mask	2022-04-28T15:28:27Z	--- inference: false --- This is a model checkpoint for ["Should You Mask 15% in Masked Language Modeling"](https://arxiv.org/abs/2202.08005) [(code)](https://github.com/princeton-nlp/DinkyTrain.git). We use pre layer norm, which is not supported by HuggingFace. To use our model, go to our [github repo](https://github.com/princeton-nlp/DinkyTrain.git), download our code, and import the RoBERTa class from `huggingface/modeling_roberta_prelayernorm.py`. For example, ``` bash from huggingface.modeling_roberta_prelayernorm import RobertaForMaskedLM, RobertaForSequenceClassification ```
princeton-nlp/efficient_mlm_m0.70	princeton-nlp	2022-04-28T18:57:57Z	4	0	transformers	[ "transformers", "pytorch", "roberta", "fill-mask", "arxiv:2202.08005", "autotrain_compatible", "region:us" ]	fill-mask	2022-04-28T15:28:36Z	--- inference: false --- This is a model checkpoint for ["Should You Mask 15% in Masked Language Modeling"](https://arxiv.org/abs/2202.08005) [(code)](https://github.com/princeton-nlp/DinkyTrain.git). We use pre layer norm, which is not supported by HuggingFace. To use our model, go to our [github repo](https://github.com/princeton-nlp/DinkyTrain.git), download our code, and import the RoBERTa class from `huggingface/modeling_roberta_prelayernorm.py`. For example, ``` bash from huggingface.modeling_roberta_prelayernorm import RobertaForMaskedLM, RobertaForSequenceClassification ```
radames/PIFu-upright-standing	radames	2022-04-28T18:31:23Z	0	3	null	[ "license:mit", "region:us" ]	null	2022-04-22T23:41:52Z	--- license: mit --- # PIFu: Pixel-Aligned Implicit Function for High-Resolution Clothed Human Digitization <a href="https://github.com/shunsukesaito/PIFu" target="_blank">https://github.com/shunsukesaito/PIFu</a> This a checkpoint from the original project here are some important <a href="https://github.com/shunsukesaito/PIFu#demo" target="_blank">notes</a>: > Warning: The released model is trained with mostly upright standing scans with weak perspectie projection and the pitch angle of 0 degree. Reconstruction quality may degrade for images highly deviated from trainining data. ``` @InProceedings{saito2019pifu, author = {Saito, Shunsuke and Huang, Zeng and Natsume, Ryota and Morishima, Shigeo and Kanazawa, Angjoo and Li, Hao}, title = {PIFu: Pixel-Aligned Implicit Function for High-Resolution Clothed Human Digitization}, booktitle = {The IEEE International Conference on Computer Vision (ICCV)}, month = {October}, year = {2019} } ```
faisalahmad/summarizer2	faisalahmad	2022-04-28T17:48:14Z	3	0	transformers	[ "transformers", "pytorch", "pegasus", "text2text-generation", "autotrain", "en", "dataset:faisalahmad/autotrain-data-nsut-nlp-project-textsummarization", "co2_eq_emissions", "autotrain_compatible", "endpoints_compatible", "region:us" ]	text2text-generation	2022-04-27T09:09:24Z	--- tags: autotrain language: en widget: - text: "I love AutoTrain 🤗" datasets: - faisalahmad/autotrain-data-nsut-nlp-project-textsummarization co2_eq_emissions: 4444.804304528572 --- # Model Trained Using AutoTrain - Problem type: Summarization - Model ID: 791824381 - CO2 Emissions (in grams): 4444.804304528572 ## Validation Metrics - Loss: 1.4599040746688843 - Rouge1: 46.5461 - Rouge2: 23.8595 - RougeL: 38.526 - RougeLsum: 38.5219 - Gen Len: 23.468 ## Usage You can use cURL to access this model: ``` $ curl -X POST -H "Authorization: Bearer YOUR_HUGGINGFACE_API_KEY" -H "Content-Type: application/json" -d '{"inputs": "I love AutoTrain"}' https://api-inference.huggingface.co/faisalahmad/autotrain-nsut-nlp-project-textsummarization-791824381 ```
Slavka/distil-bert-finetuned-log-parser-winlogbeat	Slavka	2022-04-28T17:46:08Z	6	0	transformers	[ "transformers", "tf", "distilbert", "question-answering", "generated_from_keras_callback", "license:apache-2.0", "endpoints_compatible", "region:us" ]	question-answering	2022-04-26T21:43:08Z	--- license: apache-2.0 tags: - generated_from_keras_callback model-index: - name: distil-bert-finetuned-log-parser-winlogbeat results: [] --- <!-- This model card has been generated automatically according to the information Keras had access to. You should probably proofread and complete it, then remove this comment. --> # distil-bert-finetuned-log-parser-winlogbeat This model is a fine-tuned version of [distilbert-base-uncased-distilled-squad](https://huggingface.co/distilbert-base-uncased-distilled-squad) on an unknown dataset. It achieves the following results on the evaluation set: ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - optimizer: {'name': 'AdamWeightDecay', 'learning_rate': {'class_name': 'PolynomialDecay', 'config': {'initial_learning_rate': 2e-05, 'decay_steps': 1635, 'end_learning_rate': 0.0, 'power': 1.0, 'cycle': False, 'name': None}}, 'decay': 0.0, 'beta_1': 0.9, 'beta_2': 0.999, 'epsilon': 1e-08, 'amsgrad': False, 'weight_decay_rate': 0.01} - training_precision: mixed_float16 ### Training results ### Framework versions - Transformers 4.18.0 - TensorFlow 2.8.0 - Datasets 2.1.0 - Tokenizers 0.12.1
juancavallotti/roberta-base-culinary-finetuned	juancavallotti	2022-04-28T17:42:59Z	12	0	transformers	[ "transformers", "pytorch", "tensorboard", "bert", "text-classification", "generated_from_trainer", "autotrain_compatible", "endpoints_compatible", "region:us" ]	text-classification	2022-04-28T17:06:44Z	--- tags: - generated_from_trainer metrics: - f1 model-index: - name: roberta-base-culinary-finetuned results: [] --- <!-- This model card has been generated automatically according to the information the Trainer had access to. You should probably proofread and complete it, then remove this comment. --> # roberta-base-culinary-finetuned This model was trained from scratch on the None dataset. It achieves the following results on the evaluation set: - Loss: 0.0657 - F1: 0.9929 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 2e-05 - train_batch_size: 8 - eval_batch_size: 8 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - num_epochs: 4 ### Training results \| Training Loss \| Epoch \| Step \| Validation Loss \| F1 \| \|:-------------:\|:-----:\|:-----:\|:---------------:\|:------:\| \| 0.1803 \| 0.11 \| 500 \| 0.1939 \| 0.9611 \| \| 0.1543 \| 0.22 \| 1000 \| 0.1364 \| 0.9669 \| \| 0.1213 \| 0.32 \| 1500 \| 0.1487 \| 0.9728 \| \| 0.1079 \| 0.43 \| 2000 \| 0.0855 \| 0.9773 \| \| 0.0975 \| 0.54 \| 2500 \| 0.0844 \| 0.9831 \| \| 0.0855 \| 0.65 \| 3000 \| 0.0785 \| 0.9831 \| \| 0.0844 \| 0.76 \| 3500 \| 0.0679 \| 0.9857 \| \| 0.0793 \| 0.86 \| 4000 \| 0.0489 \| 0.9890 \| \| 0.0864 \| 0.97 \| 4500 \| 0.0399 \| 0.9903 \| \| 0.049 \| 1.08 \| 5000 \| 0.0528 \| 0.9890 \| \| 0.0353 \| 1.19 \| 5500 \| 0.0635 \| 0.9877 \| \| 0.0321 \| 1.3 \| 6000 \| 0.0542 \| 0.9903 \| \| 0.0311 \| 1.41 \| 6500 \| 0.0559 \| 0.9896 \| \| 0.0315 \| 1.51 \| 7000 \| 0.0736 \| 0.9857 \| \| 0.04 \| 1.62 \| 7500 \| 0.0648 \| 0.9909 \| \| 0.0265 \| 1.73 \| 8000 \| 0.0608 \| 0.9909 \| \| 0.0443 \| 1.84 \| 8500 \| 0.0617 \| 0.9883 \| \| 0.0443 \| 1.95 \| 9000 \| 0.0555 \| 0.9896 \| \| 0.0235 \| 2.05 \| 9500 \| 0.0608 \| 0.9903 \| \| 0.0139 \| 2.16 \| 10000 \| 0.0613 \| 0.9922 \| \| 0.0126 \| 2.27 \| 10500 \| 0.0739 \| 0.9903 \| \| 0.0164 \| 2.38 \| 11000 \| 0.0679 \| 0.9903 \| \| 0.0172 \| 2.49 \| 11500 \| 0.0606 \| 0.9922 \| \| 0.0175 \| 2.59 \| 12000 \| 0.0442 \| 0.9942 \| \| 0.01 \| 2.7 \| 12500 \| 0.0661 \| 0.9916 \| \| 0.0059 \| 2.81 \| 13000 \| 0.0659 \| 0.9929 \| \| 0.0216 \| 2.92 \| 13500 \| 0.0504 \| 0.9929 \| \| 0.0123 \| 3.03 \| 14000 \| 0.0584 \| 0.9929 \| \| 0.0047 \| 3.14 \| 14500 \| 0.0573 \| 0.9929 \| \| 0.0123 \| 3.24 \| 15000 \| 0.0511 \| 0.9935 \| \| 0.0027 \| 3.35 \| 15500 \| 0.0579 \| 0.9942 \| \| 0.0025 \| 3.46 \| 16000 \| 0.0602 \| 0.9935 \| \| 0.0051 \| 3.57 \| 16500 \| 0.0598 \| 0.9935 \| \| 0.0044 \| 3.68 \| 17000 \| 0.0617 \| 0.9929 \| \| 0.0061 \| 3.78 \| 17500 \| 0.0634 \| 0.9935 \| \| 0.0048 \| 3.89 \| 18000 \| 0.0672 \| 0.9929 \| \| 0.0078 \| 4.0 \| 18500 \| 0.0657 \| 0.9929 \| ### Framework versions - Transformers 4.18.0 - Pytorch 1.11.0+cu113 - Datasets 2.1.0 - Tokenizers 0.12.1
stevems1/bert-base-uncased-ShreeGanesh	stevems1	2022-04-28T15:16:26Z	4	0	transformers	[ "transformers", "pytorch", "tensorboard", "bert", "fill-mask", "generated_from_trainer", "autotrain_compatible", "endpoints_compatible", "region:us" ]	fill-mask	2022-04-28T14:19:50Z	--- tags: - generated_from_trainer model-index: - name: bert-base-uncased-ShreeGanesh results: [] --- <!-- This model card has been generated automatically according to the information the Trainer had access to. You should probably proofread and complete it, then remove this comment. --> # bert-base-uncased-ShreeGanesh This model is a fine-tuned version of [](https://huggingface.co/) on an unknown dataset. ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 5e-05 - train_batch_size: 32 - eval_batch_size: 8 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - num_epochs: 1 ### Training results ### Framework versions - Transformers 4.18.0 - Pytorch 1.11.0+cu113 - Datasets 2.1.0 - Tokenizers 0.12.1
aakarshan/autotrain-Question-translation-797524592	aakarshan	2022-04-28T14:48:38Z	4	0	transformers	[ "transformers", "pytorch", "mt5", "text2text-generation", "autotrain", "translation", "en", "hi", "dataset:aakarshan/autotrain-data-Question-translation", "co2_eq_emissions", "autotrain_compatible", "endpoints_compatible", "region:us" ]	translation	2022-04-28T14:26:14Z	--- tags: - autotrain - translation language: - en - hi datasets: - aakarshan/autotrain-data-Question-translation co2_eq_emissions: 27.564419884224776 --- # Model Trained Using AutoTrain - Problem type: Translation - Model ID: 797524592 - CO2 Emissions (in grams): 27.564419884224776 ## Validation Metrics - Loss: 2.2697999477386475 - SacreBLEU: 14.9797 - Gen len: 13.7071
nlpaueb/sec-bert-shape	nlpaueb	2022-04-28T14:46:51Z	45	14	transformers	[ "transformers", "pytorch", "tf", "bert", "pretraining", "finance", "financial", "fill-mask", "en", "arxiv:2203.06482", "license:cc-by-sa-4.0", "endpoints_compatible", "region:us" ]	fill-mask	2022-03-02T23:29:05Z	--- language: en pipeline_tag: fill-mask license: cc-by-sa-4.0 thumbnail: https://i.ibb.co/0yz81K9/sec-bert-logo.png tags: - finance - financial widget: - text: "Total net sales decreased [MASK]% or $[X.X] billion during [XXXX] compared to [XXXX]" - text: "Total net sales decreased [X]% or $[MASK] billion during [XXXX] compared to [XXXX]." - text: "Total net sales decreased [X]% or $[X.X] billion during [MASK] compared to [XXXX]." - text: "During [MASK], the Company repurchased $[XX.X] billion of its common stock and paid dividend equivalents of $[XX.X] billion." - text: "During 2019, the Company repurchased $[MASK] billion of its common stock and paid dividend equivalents of $[XX.X] billion." --- # SEC-BERT <img align="center" src="https://i.ibb.co/0yz81K9/sec-bert-logo.png" alt="sec-bert-logo" width="400"/> <div style="text-align: justify"> SEC-BERT is a family of BERT models for the financial domain, intended to assist financial NLP research and FinTech applications. SEC-BERT consists of the following models: * [SEC-BERT-BASE](https://huggingface.co/nlpaueb/sec-bert-base): Same architecture as BERT-BASE trained on financial documents. * [SEC-BERT-NUM](https://huggingface.co/nlpaueb/sec-bert-num): Same as SEC-BERT-BASE but we replace every number token with a [NUM] pseudo-token handling all numeric expressions in a uniform manner, disallowing their fragmentation) * SEC-BERT-SHAPE (this model): Same as SEC-BERT-BASE but we replace numbers with pseudo-tokens that represent the number’s shape, so numeric expressions (of known shapes) are no longer fragmented, e.g., '53.2' becomes '[XX.X]' and '40,200.5' becomes '[XX,XXX.X]'. </div> ## Pre-training corpus The model was pre-trained on 260,773 10-K filings from 1993-2019, publicly available at <a href="https://www.sec.gov/">U.S. Securities and Exchange Commission (SEC)</a> ## Pre-training details <div style="text-align: justify"> * We created a new vocabulary of 30k subwords by training a [BertWordPieceTokenizer](https://github.com/huggingface/tokenizers) from scratch on the pre-training corpus. * We trained BERT using the official code provided in [Google BERT's GitHub repository](https://github.com/google-research/bert)</a>. * We then used [Hugging Face](https://huggingface.co)'s [Transformers](https://github.com/huggingface/transformers) conversion script to convert the TF checkpoint in the desired format in order to be able to load the model in two lines of code for both PyTorch and TF2 users. * We release a model similar to the English BERT-BASE model (12-layer, 768-hidden, 12-heads, 110M parameters). * We chose to follow the same training set-up: 1 million training steps with batches of 256 sequences of length 512 with an initial learning rate 1e-4. * We were able to use a single Google Cloud TPU v3-8 provided for free from [TensorFlow Research Cloud (TRC)](https://sites.research.google/trc), while also utilizing [GCP research credits](https://edu.google.com/programs/credits/research). Huge thanks to both Google programs for supporting us! </div> ## Load Pretrained Model ```python from transformers import AutoTokenizer, AutoModel tokenizer = AutoTokenizer.from_pretrained("nlpaueb/sec-bert-shape") model = AutoModel.from_pretrained("nlpaueb/sec-bert-shape") ``` ## Pre-process Text <div style="text-align: justify"> To use SEC-BERT-SHAPE, you have to pre-process texts replacing every numerical token with the corresponding shape pseudo-token, from a list of 214 predefined shape pseudo-tokens. If the numerical token does not correspond to any shape pseudo-token we replace it with the [NUM] pseudo-token. Below there is an example of how you can pre-process a simple sentence. This approach is quite simple; feel free to modify it as you see fit. </div> ```python import re import spacy from transformers import AutoTokenizer tokenizer = AutoTokenizer.from_pretrained("nlpaueb/sec-bert-shape") spacy_tokenizer = spacy.load("en_core_web_sm") sentence = "Total net sales decreased 2% or $5.4 billion during 2019 compared to 2018." def sec_bert_shape_preprocess(text): tokens = [t.text for t in spacy_tokenizer(sentence)] processed_text = [] for token in tokens: if re.fullmatch(r"(\d+[\d,.])\|([,.]\d+)", token): shape = '[' + re.sub(r'\d', 'X', token) + ']' if shape in tokenizer.additional_special_tokens: processed_text.append(shape) else: processed_text.append('[NUM]') else: processed_text.append(token) return ' '.join(processed_text) tokenized_sentence = tokenizer.tokenize(sec_bert_shape_preprocess(sentence)) print(tokenized_sentence) """ ['total', 'net', 'sales', 'decreased', '[X]', '%', 'or', '$', '[X.X]', 'billion', 'during', '[XXXX]', 'compared', 'to', '[XXXX]', '.'] """ ``` ## Using SEC-BERT variants as Language Models \| Sample \| Masked Token \| \| --------------------------------------------------- \| ------------ \| \| Total net sales [MASK] 2% or $5.4 billion during 2019 compared to 2018. \| decreased \| Model \| Predictions (Probability) \| \| --------------------------------------------------- \| ------------ \| \| BERT-BASE-UNCASED* \| increased (0.221), were (0.131), are (0.103), rose (0.075), of (0.058) \| SEC-BERT-BASE \| increased (0.678), decreased (0.282), declined (0.017), grew (0.016), rose (0.004) \| SEC-BERT-NUM \| increased (0.753), decreased (0.211), grew (0.019), declined (0.010), rose (0.006) \| SEC-BERT-SHAPE \| increased (0.747), decreased (0.214), grew (0.021), declined (0.013), rose (0.002) \| Sample \| Masked Token \| \| --------------------------------------------------- \| ------------ \| \| Total net sales decreased 2% or $5.4 [MASK] during 2019 compared to 2018. \| billion \| Model \| Predictions (Probability) \| \| --------------------------------------------------- \| ------------ \| \| BERT-BASE-UNCASED \| billion (0.841), million (0.097), trillion (0.028), ##m (0.015), ##bn (0.006) \| SEC-BERT-BASE \| million (0.972), billion (0.028), millions (0.000), ##million (0.000), m (0.000) \| SEC-BERT-NUM \| million (0.974), billion (0.012), , (0.010), thousand (0.003), m (0.000) \| SEC-BERT-SHAPE \| million (0.978), billion (0.021), % (0.000), , (0.000), millions (0.000) \| Sample \| Masked Token \| \| --------------------------------------------------- \| ------------ \| \| Total net sales decreased [MASK]% or $5.4 billion during 2019 compared to 2018. \| 2 \| Model \| Predictions (Probability) \| \| --------------------------------------------------- \| ------------ \| \| BERT-BASE-UNCASED \| 20 (0.031), 10 (0.030), 6 (0.029), 4 (0.027), 30 (0.027) \| SEC-BERT-BASE \| 13 (0.045), 12 (0.040), 11 (0.040), 14 (0.035), 10 (0.035) \| SEC-BERT-NUM \| [NUM] (1.000), one (0.000), five (0.000), three (0.000), seven (0.000) \| SEC-BERT-SHAPE \| [XX] (0.316), [XX.X] (0.253), [X.X] (0.237), [X] (0.188), [X.XX] (0.002) \| Sample \| Masked Token \| \| --------------------------------------------------- \| ------------ \| \| Total net sales decreased 2[MASK] or $5.4 billion during 2019 compared to 2018. \| % \| Model \| Predictions (Probability) \| \| --------------------------------------------------- \| ------------ \| \| BERT-BASE-UNCASED \| % (0.795), percent (0.174), ##fold (0.009), billion (0.004), times (0.004) \| SEC-BERT-BASE \| % (0.924), percent (0.076), points (0.000), , (0.000), times (0.000) \| SEC-BERT-NUM \| % (0.882), percent (0.118), million (0.000), units (0.000), bps (0.000) \| SEC-BERT-SHAPE \| % (0.961), percent (0.039), bps (0.000), , (0.000), bcf (0.000) \| Sample \| Masked Token \| \| --------------------------------------------------- \| ------------ \| \| Total net sales decreased 2% or $[MASK] billion during 2019 compared to 2018. \| 5.4 \| Model \| Predictions (Probability) \| \| --------------------------------------------------- \| ------------ \| \| BERT-BASE-UNCASED \| 1 (0.074), 4 (0.045), 3 (0.044), 2 (0.037), 5 (0.034) \| SEC-BERT-BASE \| 1 (0.218), 2 (0.136), 3 (0.078), 4 (0.066), 5 (0.048) \| SEC-BERT-NUM \| [NUM] (1.000), l (0.000), 1 (0.000), - (0.000), 30 (0.000) \| SEC-BERT-SHAPE \| [X.X] (0.787), [X.XX] (0.095), [XX.X] (0.049), [X.XXX] (0.046), [X] (0.013) \| Sample \| Masked Token \| \| --------------------------------------------------- \| ------------ \| \| Total net sales decreased 2% or $5.4 billion during [MASK] compared to 2018. \| 2019 \| Model \| Predictions (Probability) \| \| --------------------------------------------------- \| ------------ \| \| BERT-BASE-UNCASED \| 2017 (0.485), 2018 (0.169), 2016 (0.164), 2015 (0.070), 2014 (0.022) \| SEC-BERT-BASE \| 2019 (0.990), 2017 (0.007), 2018 (0.003), 2020 (0.000), 2015 (0.000) \| SEC-BERT-NUM \| [NUM] (1.000), as (0.000), fiscal (0.000), year (0.000), when (0.000) \| SEC-BERT-SHAPE \| [XXXX] (1.000), as (0.000), year (0.000), periods (0.000), , (0.000) \| Sample \| Masked Token \| \| --------------------------------------------------- \| ------------ \| \| Total net sales decreased 2% or $5.4 billion during 2019 compared to [MASK]. \| 2018 \| Model \| Predictions (Probability) \| \| --------------------------------------------------- \| ------------ \| \| BERT-BASE-UNCASED \| 2017 (0.100), 2016 (0.097), above (0.054), inflation (0.050), previously (0.037) \| SEC-BERT-BASE \| 2018 (0.999), 2019 (0.000), 2017 (0.000), 2016 (0.000), 2014 (0.000) \| SEC-BERT-NUM \| [NUM] (1.000), year (0.000), last (0.000), sales (0.000), fiscal (0.000) \| SEC-BERT-SHAPE \| [XXXX] (1.000), year (0.000), sales (0.000), prior (0.000), years (0.000) \| Sample \| Masked Token \| \| --------------------------------------------------- \| ------------ \| \| During 2019, the Company [MASK] $67.1 billion of its common stock and paid dividend equivalents of $14.1 billion. \| repurchased \| Model \| Predictions (Probability) \| \| --------------------------------------------------- \| ------------ \| \| BERT-BASE-UNCASED \| held (0.229), sold (0.192), acquired (0.172), owned (0.052), traded (0.033) \| SEC-BERT-BASE \| repurchased (0.913), issued (0.036), purchased (0.029), redeemed (0.010), sold (0.003) \| SEC-BERT-NUM \| repurchased (0.917), purchased (0.054), reacquired (0.013), issued (0.005), acquired (0.003) \| SEC-BERT-SHAPE \| repurchased (0.902), purchased (0.068), issued (0.010), reacquired (0.008), redeemed (0.006) \| Sample \| Masked Token \| \| --------------------------------------------------- \| ------------ \| \| During 2019, the Company repurchased $67.1 billion of its common [MASK] and paid dividend equivalents of $14.1 billion. \| stock \| Model \| Predictions (Probability) \| \| --------------------------------------------------- \| ------------ \| \| BERT-BASE-UNCASED \| stock (0.835), assets (0.039), equity (0.025), debt (0.021), bonds (0.017) \| SEC-BERT-BASE \| stock (0.857), shares (0.135), equity (0.004), units (0.002), securities (0.000) \| SEC-BERT-NUM \| stock (0.842), shares (0.157), equity (0.000), securities (0.000), units (0.000) \| SEC-BERT-SHAPE \| stock (0.888), shares (0.109), equity (0.001), securities (0.001), stocks (0.000) \| Sample \| Masked Token \| \| --------------------------------------------------- \| ------------ \| \| During 2019, the Company repurchased $67.1 billion of its common stock and paid [MASK] equivalents of $14.1 billion. \| dividend \| Model \| Predictions (Probability) \| \| --------------------------------------------------- \| ------------ \| \| BERT-BASE-UNCASED \| cash (0.276), net (0.128), annual (0.083), the (0.040), debt (0.027) \| SEC-BERT-BASE \| dividend (0.890), cash (0.018), dividends (0.016), share (0.013), tax (0.010) \| SEC-BERT-NUM \| dividend (0.735), cash (0.115), share (0.087), tax (0.025), stock (0.013) \| SEC-BERT-SHAPE \| dividend (0.655), cash (0.248), dividends (0.042), share (0.019), out (0.003) \| Sample \| Masked Token \| \| --------------------------------------------------- \| ------------ \| \| During 2019, the Company repurchased $67.1 billion of its common stock and paid dividend [MASK] of $14.1 billion. \| equivalents \| Model \| Predictions (Probability) \| \| --------------------------------------------------- \| ------------ \| \| BERT-BASE-UNCASED \| revenue (0.085), earnings (0.078), rates (0.065), amounts (0.064), proceeds (0.062) \| SEC-BERT-BASE \| payments (0.790), distributions (0.087), equivalents (0.068), cash (0.013), amounts (0.004) \| SEC-BERT-NUM \| payments (0.845), equivalents (0.097), distributions (0.024), increases (0.005), dividends (0.004) \| SEC-BERT-SHAPE \| payments (0.784), equivalents (0.093), distributions (0.043), dividends (0.015), requirements (0.009) ## Publication <div style="text-align: justify"> If you use this model cite the following article:<br> [FiNER: Financial Numeric Entity Recognition for XBRL Tagging](https://arxiv.org/abs/2203.06482)<br> Lefteris Loukas, Manos Fergadiotis, Ilias Chalkidis, Eirini Spyropoulou, Prodromos Malakasiotis, Ion Androutsopoulos and George Paliouras<br> In the Proceedings of the 60th Annual Meeting of the Association for Computational Linguistics (ACL 2022) (Long Papers), Dublin, Republic of Ireland, May 22 - 27, 2022 </div> ``` @inproceedings{loukas-etal-2022-finer, title = {FiNER: Financial Numeric Entity Recognition for XBRL Tagging}, author = {Loukas, Lefteris and Fergadiotis, Manos and Chalkidis, Ilias and Spyropoulou, Eirini and Malakasiotis, Prodromos and Androutsopoulos, Ion and Paliouras George}, booktitle = {Proceedings of the 60th Annual Meeting of the Association for Computational Linguistics (ACL 2022)}, publisher = {Association for Computational Linguistics}, location = {Dublin, Republic of Ireland}, year = {2022}, url = {https://arxiv.org/abs/2203.06482} } ``` ## About Us <div style="text-align: justify"> [AUEB's Natural Language Processing Group](http://nlp.cs.aueb.gr) develops algorithms, models, and systems that allow computers to process and generate natural language texts. The group's current research interests include: * question answering systems for databases, ontologies, document collections, and the Web, especially biomedical question answering, * natural language generation from databases and ontologies, especially Semantic Web ontologies, text classification, including filtering spam and abusive content, * information extraction and opinion mining, including legal text analytics and sentiment analysis, * natural language processing tools for Greek, for example parsers and named-entity recognizers, machine learning in natural language processing, especially deep learning. The group is part of the Information Processing Laboratory of the Department of Informatics of the Athens University of Economics and Business. </div> [Manos Fergadiotis](https://manosfer.github.io) on behalf of [AUEB's Natural Language Processing Group](http://nlp.cs.aueb.gr)
nlpaueb/sec-bert-num	nlpaueb	2022-04-28T14:46:16Z	27	6	transformers	[ "transformers", "pytorch", "tf", "bert", "pretraining", "finance", "financial", "fill-mask", "en", "arxiv:2203.06482", "license:cc-by-sa-4.0", "endpoints_compatible", "region:us" ]	fill-mask	2022-03-02T23:29:05Z	--- language: en pipeline_tag: fill-mask license: cc-by-sa-4.0 thumbnail: https://i.ibb.co/0yz81K9/sec-bert-logo.png tags: - finance - financial widget: - text: "Total net sales decreased [MASK]% or $[NUM] billion during [NUM] compared to [NUM]." - text: "Total net sales decreased [NUM]% or $[MASK] billion during [NUM] compared to [NUM]." - text: "Total net sales decreased [NUM]% or $[NUM] billion during [MASK] compared to [NUM]." - text: "During [MASK], the Company repurchased $[NUM] billion of its common stock and paid dividend equivalents of $[NUM] billion." - text: "During 2019, the Company repurchased $[MASK] billion of its common stock and paid dividend equivalents of $[NUM] billion." --- # SEC-BERT <img align="center" src="https://i.ibb.co/0yz81K9/sec-bert-logo.png" alt="sec-bert-logo" width="400"/> <div style="text-align: justify"> SEC-BERT is a family of BERT models for the financial domain, intended to assist financial NLP research and FinTech applications. SEC-BERT consists of the following models: * [SEC-BERT-BASE](https://huggingface.co/nlpaueb/sec-bert-base): Same architecture as BERT-BASE trained on financial documents. * SEC-BERT-NUM (this model): Same as SEC-BERT-BASE but we replace every number token with a [NUM] pseudo-token handling all numeric expressions in a uniform manner, disallowing their fragmentation). * [SEC-BERT-SHAPE](https://huggingface.co/nlpaueb/sec-bert-shape): Same as SEC-BERT-BASE but we replace numbers with pseudo-tokens that represent the number’s shape, so numeric expressions (of known shapes) are no longer fragmented, e.g., '53.2' becomes '[XX.X]' and '40,200.5' becomes '[XX,XXX.X]'. </div> ## Pre-training corpus The model was pre-trained on 260,773 10-K filings from 1993-2019, publicly available at <a href="https://www.sec.gov/">U.S. Securities and Exchange Commission (SEC)</a> ## Pre-training details <div style="text-align: justify"> * We created a new vocabulary of 30k subwords by training a [BertWordPieceTokenizer](https://github.com/huggingface/tokenizers) from scratch on the pre-training corpus. * We trained BERT using the official code provided in [Google BERT's GitHub repository](https://github.com/google-research/bert)</a>. * We then used [Hugging Face](https://huggingface.co)'s [Transformers](https://github.com/huggingface/transformers) conversion script to convert the TF checkpoint in the desired format in order to be able to load the model in two lines of code for both PyTorch and TF2 users. * We release a model similar to the English BERT-BASE model (12-layer, 768-hidden, 12-heads, 110M parameters). * We chose to follow the same training set-up: 1 million training steps with batches of 256 sequences of length 512 with an initial learning rate 1e-4. * We were able to use a single Google Cloud TPU v3-8 provided for free from [TensorFlow Research Cloud (TRC)](https://sites.research.google/trc), while also utilizing [GCP research credits](https://edu.google.com/programs/credits/research). Huge thanks to both Google programs for supporting us! </div> ## Load Pretrained Model ```python from transformers import AutoTokenizer, AutoModel tokenizer = AutoTokenizer.from_pretrained("nlpaueb/sec-bert-num") model = AutoModel.from_pretrained("nlpaueb/sec-bert-num") ``` ## Pre-process Text <div style="text-align: justify"> To use SEC-BERT-NUM, you have to pre-process texts replacing every numerical token with [NUM] pseudo-token. Below there is an example of how you can pre-process a simple sentence. This approach is quite simple; feel free to modify it as you see fit. </div> ```python import re import spacy from transformers import AutoTokenizer tokenizer = AutoTokenizer.from_pretrained("nlpaueb/sec-bert-num") spacy_tokenizer = spacy.load("en_core_web_sm") sentence = "Total net sales decreased 2% or $5.4 billion during 2019 compared to 2018." def sec_bert_num_preprocess(text): tokens = [t.text for t in spacy_tokenizer(text)] processed_text = [] for token in tokens: if re.fullmatch(r"(\d+[\d,.])\|([,.]\d+)", token): processed_text.append('[NUM]') else: processed_text.append(token) return ' '.join(processed_text) tokenized_sentence = tokenizer.tokenize(sec_bert_num_preprocess(sentence)) print(tokenized_sentence) """ ['total', 'net', 'sales', 'decreased', '[NUM]', '%', 'or', '$', '[NUM]', 'billion', 'during', '[NUM]', 'compared', 'to', '[NUM]', '.'] """ ``` ## Using SEC-BERT variants as Language Models \| Sample \| Masked Token \| \| --------------------------------------------------- \| ------------ \| \| Total net sales [MASK] 2% or $5.4 billion during 2019 compared to 2018. \| decreased \| Model \| Predictions (Probability) \| \| --------------------------------------------------- \| ------------ \| \| BERT-BASE-UNCASED* \| increased (0.221), were (0.131), are (0.103), rose (0.075), of (0.058) \| SEC-BERT-BASE \| increased (0.678), decreased (0.282), declined (0.017), grew (0.016), rose (0.004) \| SEC-BERT-NUM \| increased (0.753), decreased (0.211), grew (0.019), declined (0.010), rose (0.006) \| SEC-BERT-SHAPE \| increased (0.747), decreased (0.214), grew (0.021), declined (0.013), rose (0.002) \| Sample \| Masked Token \| \| --------------------------------------------------- \| ------------ \| \| Total net sales decreased 2% or $5.4 [MASK] during 2019 compared to 2018. \| billion \| Model \| Predictions (Probability) \| \| --------------------------------------------------- \| ------------ \| \| BERT-BASE-UNCASED \| billion (0.841), million (0.097), trillion (0.028), ##m (0.015), ##bn (0.006) \| SEC-BERT-BASE \| million (0.972), billion (0.028), millions (0.000), ##million (0.000), m (0.000) \| SEC-BERT-NUM \| million (0.974), billion (0.012), , (0.010), thousand (0.003), m (0.000) \| SEC-BERT-SHAPE \| million (0.978), billion (0.021), % (0.000), , (0.000), millions (0.000) \| Sample \| Masked Token \| \| --------------------------------------------------- \| ------------ \| \| Total net sales decreased [MASK]% or $5.4 billion during 2019 compared to 2018. \| 2 \| Model \| Predictions (Probability) \| \| --------------------------------------------------- \| ------------ \| \| BERT-BASE-UNCASED \| 20 (0.031), 10 (0.030), 6 (0.029), 4 (0.027), 30 (0.027) \| SEC-BERT-BASE \| 13 (0.045), 12 (0.040), 11 (0.040), 14 (0.035), 10 (0.035) \| SEC-BERT-NUM \| [NUM] (1.000), one (0.000), five (0.000), three (0.000), seven (0.000) \| SEC-BERT-SHAPE \| [XX] (0.316), [XX.X] (0.253), [X.X] (0.237), [X] (0.188), [X.XX] (0.002) \| Sample \| Masked Token \| \| --------------------------------------------------- \| ------------ \| \| Total net sales decreased 2[MASK] or $5.4 billion during 2019 compared to 2018. \| % \| Model \| Predictions (Probability) \| \| --------------------------------------------------- \| ------------ \| \| BERT-BASE-UNCASED \| % (0.795), percent (0.174), ##fold (0.009), billion (0.004), times (0.004) \| SEC-BERT-BASE \| % (0.924), percent (0.076), points (0.000), , (0.000), times (0.000) \| SEC-BERT-NUM \| % (0.882), percent (0.118), million (0.000), units (0.000), bps (0.000) \| SEC-BERT-SHAPE \| % (0.961), percent (0.039), bps (0.000), , (0.000), bcf (0.000) \| Sample \| Masked Token \| \| --------------------------------------------------- \| ------------ \| \| Total net sales decreased 2% or $[MASK] billion during 2019 compared to 2018. \| 5.4 \| Model \| Predictions (Probability) \| \| --------------------------------------------------- \| ------------ \| \| BERT-BASE-UNCASED \| 1 (0.074), 4 (0.045), 3 (0.044), 2 (0.037), 5 (0.034) \| SEC-BERT-BASE \| 1 (0.218), 2 (0.136), 3 (0.078), 4 (0.066), 5 (0.048) \| SEC-BERT-NUM \| [NUM] (1.000), l (0.000), 1 (0.000), - (0.000), 30 (0.000) \| SEC-BERT-SHAPE \| [X.X] (0.787), [X.XX] (0.095), [XX.X] (0.049), [X.XXX] (0.046), [X] (0.013) \| Sample \| Masked Token \| \| --------------------------------------------------- \| ------------ \| \| Total net sales decreased 2% or $5.4 billion during [MASK] compared to 2018. \| 2019 \| Model \| Predictions (Probability) \| \| --------------------------------------------------- \| ------------ \| \| BERT-BASE-UNCASED \| 2017 (0.485), 2018 (0.169), 2016 (0.164), 2015 (0.070), 2014 (0.022) \| SEC-BERT-BASE \| 2019 (0.990), 2017 (0.007), 2018 (0.003), 2020 (0.000), 2015 (0.000) \| SEC-BERT-NUM \| [NUM] (1.000), as (0.000), fiscal (0.000), year (0.000), when (0.000) \| SEC-BERT-SHAPE \| [XXXX] (1.000), as (0.000), year (0.000), periods (0.000), , (0.000) \| Sample \| Masked Token \| \| --------------------------------------------------- \| ------------ \| \| Total net sales decreased 2% or $5.4 billion during 2019 compared to [MASK]. \| 2018 \| Model \| Predictions (Probability) \| \| --------------------------------------------------- \| ------------ \| \| BERT-BASE-UNCASED \| 2017 (0.100), 2016 (0.097), above (0.054), inflation (0.050), previously (0.037) \| SEC-BERT-BASE \| 2018 (0.999), 2019 (0.000), 2017 (0.000), 2016 (0.000), 2014 (0.000) \| SEC-BERT-NUM \| [NUM] (1.000), year (0.000), last (0.000), sales (0.000), fiscal (0.000) \| SEC-BERT-SHAPE \| [XXXX] (1.000), year (0.000), sales (0.000), prior (0.000), years (0.000) \| Sample \| Masked Token \| \| --------------------------------------------------- \| ------------ \| \| During 2019, the Company [MASK] $67.1 billion of its common stock and paid dividend equivalents of $14.1 billion. \| repurchased \| Model \| Predictions (Probability) \| \| --------------------------------------------------- \| ------------ \| \| BERT-BASE-UNCASED \| held (0.229), sold (0.192), acquired (0.172), owned (0.052), traded (0.033) \| SEC-BERT-BASE \| repurchased (0.913), issued (0.036), purchased (0.029), redeemed (0.010), sold (0.003) \| SEC-BERT-NUM \| repurchased (0.917), purchased (0.054), reacquired (0.013), issued (0.005), acquired (0.003) \| SEC-BERT-SHAPE \| repurchased (0.902), purchased (0.068), issued (0.010), reacquired (0.008), redeemed (0.006) \| Sample \| Masked Token \| \| --------------------------------------------------- \| ------------ \| \| During 2019, the Company repurchased $67.1 billion of its common [MASK] and paid dividend equivalents of $14.1 billion. \| stock \| Model \| Predictions (Probability) \| \| --------------------------------------------------- \| ------------ \| \| BERT-BASE-UNCASED \| stock (0.835), assets (0.039), equity (0.025), debt (0.021), bonds (0.017) \| SEC-BERT-BASE \| stock (0.857), shares (0.135), equity (0.004), units (0.002), securities (0.000) \| SEC-BERT-NUM \| stock (0.842), shares (0.157), equity (0.000), securities (0.000), units (0.000) \| SEC-BERT-SHAPE \| stock (0.888), shares (0.109), equity (0.001), securities (0.001), stocks (0.000) \| Sample \| Masked Token \| \| --------------------------------------------------- \| ------------ \| \| During 2019, the Company repurchased $67.1 billion of its common stock and paid [MASK] equivalents of $14.1 billion. \| dividend \| Model \| Predictions (Probability) \| \| --------------------------------------------------- \| ------------ \| \| BERT-BASE-UNCASED \| cash (0.276), net (0.128), annual (0.083), the (0.040), debt (0.027) \| SEC-BERT-BASE \| dividend (0.890), cash (0.018), dividends (0.016), share (0.013), tax (0.010) \| SEC-BERT-NUM \| dividend (0.735), cash (0.115), share (0.087), tax (0.025), stock (0.013) \| SEC-BERT-SHAPE \| dividend (0.655), cash (0.248), dividends (0.042), share (0.019), out (0.003) \| Sample \| Masked Token \| \| --------------------------------------------------- \| ------------ \| \| During 2019, the Company repurchased $67.1 billion of its common stock and paid dividend [MASK] of $14.1 billion. \| equivalents \| Model \| Predictions (Probability) \| \| --------------------------------------------------- \| ------------ \| \| BERT-BASE-UNCASED \| revenue (0.085), earnings (0.078), rates (0.065), amounts (0.064), proceeds (0.062) \| SEC-BERT-BASE \| payments (0.790), distributions (0.087), equivalents (0.068), cash (0.013), amounts (0.004) \| SEC-BERT-NUM \| payments (0.845), equivalents (0.097), distributions (0.024), increases (0.005), dividends (0.004) \| SEC-BERT-SHAPE \| payments (0.784), equivalents (0.093), distributions (0.043), dividends (0.015), requirements (0.009) ## Publication <div style="text-align: justify"> If you use this model cite the following article:<br> [FiNER: Financial Numeric Entity Recognition for XBRL Tagging](https://arxiv.org/abs/2203.06482)<br> Lefteris Loukas, Manos Fergadiotis, Ilias Chalkidis, Eirini Spyropoulou, Prodromos Malakasiotis, Ion Androutsopoulos and George Paliouras<br> In the Proceedings of the 60th Annual Meeting of the Association for Computational Linguistics (ACL 2022) (Long Papers), Dublin, Republic of Ireland, May 22 - 27, 2022 </div> ``` @inproceedings{loukas-etal-2022-finer, title = {FiNER: Financial Numeric Entity Recognition for XBRL Tagging}, author = {Loukas, Lefteris and Fergadiotis, Manos and Chalkidis, Ilias and Spyropoulou, Eirini and Malakasiotis, Prodromos and Androutsopoulos, Ion and Paliouras George}, booktitle = {Proceedings of the 60th Annual Meeting of the Association for Computational Linguistics (ACL 2022)}, publisher = {Association for Computational Linguistics}, location = {Dublin, Republic of Ireland}, year = {2022}, url = {https://arxiv.org/abs/2203.06482} } ``` ## About Us <div style="text-align: justify"> [AUEB's Natural Language Processing Group](http://nlp.cs.aueb.gr) develops algorithms, models, and systems that allow computers to process and generate natural language texts. The group's current research interests include: * question answering systems for databases, ontologies, document collections, and the Web, especially biomedical question answering, * natural language generation from databases and ontologies, especially Semantic Web ontologies, text classification, including filtering spam and abusive content, * information extraction and opinion mining, including legal text analytics and sentiment analysis, * natural language processing tools for Greek, for example parsers and named-entity recognizers, machine learning in natural language processing, especially deep learning. The group is part of the Information Processing Laboratory of the Department of Informatics of the Athens University of Economics and Business. </div> [Manos Fergadiotis](https://manosfer.github.io) on behalf of [AUEB's Natural Language Processing Group](http://nlp.cs.aueb.gr)
nlpaueb/bert-base-uncased-contracts	nlpaueb	2022-04-28T14:43:56Z	44,445	26	transformers	[ "transformers", "pytorch", "tf", "jax", "bert", "legal", "fill-mask", "en", "license:cc-by-sa-4.0", "endpoints_compatible", "region:us" ]	fill-mask	2022-03-02T23:29:05Z	--- language: en pipeline_tag: fill-mask license: cc-by-sa-4.0 thumbnail: https://i.ibb.co/p3kQ7Rw/Screenshot-2020-10-06-at-12-16-36-PM.png tags: - legal widget: - text: "This [MASK] Agreement is between General Motors and John Murray." --- # LEGAL-BERT: The Muppets straight out of Law School <img align="left" src="https://i.ibb.co/p3kQ7Rw/Screenshot-2020-10-06-at-12-16-36-PM.png" width="100"/> LEGAL-BERT is a family of BERT models for the legal domain, intended to assist legal NLP research, computational law, and legal technology applications. To pre-train the different variations of LEGAL-BERT, we collected 12 GB of diverse English legal text from several fields (e.g., legislation, court cases, contracts) scraped from publicly available resources. Sub-domain variants (CONTRACTS-, EURLEX-, ECHR-) and/or general LEGAL-BERT perform better than using BERT out of the box for domain-specific tasks.<br> This is the sub-domain variant pre-trained on US contracts. <br/><br/> --- I. Chalkidis, M. Fergadiotis, P. Malakasiotis, N. Aletras and I. Androutsopoulos. "LEGAL-BERT: The Muppets straight out of Law School". In Findings of Empirical Methods in Natural Language Processing (EMNLP 2020) (Short Papers), to be held online, 2020. (https://aclanthology.org/2020.findings-emnlp.261) --- ## Pre-training corpora The pre-training corpora of LEGAL-BERT include: * 116,062 documents of EU legislation, publicly available from EURLEX (http://eur-lex.europa.eu), the repository of EU Law running under the EU Publication Office. * 61,826 documents of UK legislation, publicly available from the UK legislation portal (http://www.legislation.gov.uk). * 19,867 cases from the European Court of Justice (ECJ), also available from EURLEX. * 12,554 cases from HUDOC, the repository of the European Court of Human Rights (ECHR) (http://hudoc.echr.coe.int/eng). * 164,141 cases from various courts across the USA, hosted in the Case Law Access Project portal (https://case.law). * 76,366 US contracts from EDGAR, the database of US Securities and Exchange Commission (SECOM) (https://www.sec.gov/edgar.shtml). ## Pre-training details * We trained BERT using the official code provided in Google BERT's GitHub repository (https://github.com/google-research/bert). * We released a model similar to the English BERT-BASE model (12-layer, 768-hidden, 12-heads, 110M parameters). * We chose to follow the same training set-up: 1 million training steps with batches of 256 sequences of length 512 with an initial learning rate 1e-4. * We were able to use a single Google Cloud TPU v3-8 provided for free from [TensorFlow Research Cloud (TFRC)](https://www.tensorflow.org/tfrc), while also utilizing [GCP research credits](https://edu.google.com/programs/credits/research). Huge thanks to both Google programs for supporting us! ## Models list \| Model name \| Model Path \| Training corpora \| \| ------------------- \| ------------------------------------ \| ------------------- \| \| CONTRACTS-BERT-BASE \| `nlpaueb/bert-base-uncased-contracts` \| US contracts \| \| EURLEX-BERT-BASE \| `nlpaueb/bert-base-uncased-eurlex` \| EU legislation \| \| ECHR-BERT-BASE \| `nlpaueb/bert-base-uncased-echr` \| ECHR cases \| \| LEGAL-BERT-BASE * \| `nlpaueb/legal-bert-base-uncased` \| All \| \| LEGAL-BERT-SMALL \| `nlpaueb/legal-bert-small-uncased` \| All \| \* LEGAL-BERT-BASE is the model referred to as LEGAL-BERT-SC in Chalkidis et al. (2020); a model trained from scratch in the legal corpora mentioned below using a newly created vocabulary by a sentence-piece tokenizer trained on the very same corpora. \\ As many of you expressed interest in the LEGAL-BERT-FP models (those relying on the original BERT-BASE checkpoint), they have been released in Archive.org (https://archive.org/details/legal_bert_fp), as these models are secondary and possibly only interesting for those who aim to dig deeper in the open questions of Chalkidis et al. (2020). ## Load Pretrained Model ```python from transformers import AutoTokenizer, AutoModel tokenizer = AutoTokenizer.from_pretrained("nlpaueb/bert-base-uncased-contracts") model = AutoModel.from_pretrained("nlpaueb/bert-base-uncased-contracts") ``` ## Use LEGAL-BERT variants as Language Models \| Corpus \| Model \| Masked token \| Predictions \| \| --------------------------------- \| ---------------------------------- \| ------------ \| ------------ \| \| \| BERT-BASE-UNCASED \| \| (Contracts) \| This [MASK] Agreement is between General Motors and John Murray . \| employment \| ('new', '0.09'), ('current', '0.04'), ('proposed', '0.03'), ('marketing', '0.03'), ('joint', '0.02') \| (ECHR) \| The applicant submitted that her husband was subjected to treatment amounting to [MASK] whilst in the custody of Adana Security Directorate \| torture \| ('torture', '0.32'), ('rape', '0.22'), ('abuse', '0.14'), ('death', '0.04'), ('violence', '0.03') \| (EURLEX) \| Establishing a system for the identification and registration of [MASK] animals and regarding the labelling of beef and beef products . \| bovine \| ('farm', '0.25'), ('livestock', '0.08'), ('draft', '0.06'), ('domestic', '0.05'), ('wild', '0.05') \| \| CONTRACTS-BERT-BASE \| \| (Contracts) \| This [MASK] Agreement is between General Motors and John Murray . \| employment \| ('letter', '0.38'), ('dealer', '0.04'), ('employment', '0.03'), ('award', '0.03'), ('contribution', '0.02') \| (ECHR) \| The applicant submitted that her husband was subjected to treatment amounting to [MASK] whilst in the custody of Adana Security Directorate \| torture \| ('death', '0.39'), ('imprisonment', '0.07'), ('contempt', '0.05'), ('being', '0.03'), ('crime', '0.02') \| (EURLEX) \| Establishing a system for the identification and registration of [MASK] animals and regarding the labelling of beef and beef products . \| bovine \| (('domestic', '0.18'), ('laboratory', '0.07'), ('household', '0.06'), ('personal', '0.06'), ('the', '0.04') \| \| EURLEX-BERT-BASE \| \| (Contracts) \| This [MASK] Agreement is between General Motors and John Murray . \| employment \| ('supply', '0.11'), ('cooperation', '0.08'), ('service', '0.07'), ('licence', '0.07'), ('distribution', '0.05') \| (ECHR) \| The applicant submitted that her husband was subjected to treatment amounting to [MASK] whilst in the custody of Adana Security Directorate \| torture \| ('torture', '0.66'), ('death', '0.07'), ('imprisonment', '0.07'), ('murder', '0.04'), ('rape', '0.02') \| (EURLEX) \| Establishing a system for the identification and registration of [MASK] animals and regarding the labelling of beef and beef products . \| bovine \| ('live', '0.43'), ('pet', '0.28'), ('certain', '0.05'), ('fur', '0.03'), ('the', '0.02') \| \| ECHR-BERT-BASE \| \| (Contracts) \| This [MASK] Agreement is between General Motors and John Murray . \| employment \| ('second', '0.24'), ('latter', '0.10'), ('draft', '0.05'), ('bilateral', '0.05'), ('arbitration', '0.04') \| (ECHR) \| The applicant submitted that her husband was subjected to treatment amounting to [MASK] whilst in the custody of Adana Security Directorate \| torture \| ('torture', '0.99'), ('death', '0.01'), ('inhuman', '0.00'), ('beating', '0.00'), ('rape', '0.00') \| (EURLEX) \| Establishing a system for the identification and registration of [MASK] animals and regarding the labelling of beef and beef products . \| bovine \| ('pet', '0.17'), ('all', '0.12'), ('slaughtered', '0.10'), ('domestic', '0.07'), ('individual', '0.05') \| \| LEGAL-BERT-BASE \| \| (Contracts) \| This [MASK] Agreement is between General Motors and John Murray . \| employment \| ('settlement', '0.26'), ('letter', '0.23'), ('dealer', '0.04'), ('master', '0.02'), ('supplemental', '0.02') \| (ECHR) \| The applicant submitted that her husband was subjected to treatment amounting to [MASK] whilst in the custody of Adana Security Directorate \| torture \| ('torture', '1.00'), ('detention', '0.00'), ('arrest', '0.00'), ('rape', '0.00'), ('death', '0.00') \| (EURLEX) \| Establishing a system for the identification and registration of [MASK] animals and regarding the labelling of beef and beef products . \| bovine \| ('live', '0.67'), ('beef', '0.17'), ('farm', '0.03'), ('pet', '0.02'), ('dairy', '0.01') \| \| LEGAL-BERT-SMALL \| \| (Contracts) \| This [MASK] Agreement is between General Motors and John Murray . \| employment \| ('license', '0.09'), ('transition', '0.08'), ('settlement', '0.04'), ('consent', '0.03'), ('letter', '0.03') \| (ECHR) \| The applicant submitted that her husband was subjected to treatment amounting to [MASK] whilst in the custody of Adana Security Directorate \| torture \| ('torture', '0.59'), ('pain', '0.05'), ('ptsd', '0.05'), ('death', '0.02'), ('tuberculosis', '0.02') \| (EURLEX) \| Establishing a system for the identification and registration of [MASK] animals and regarding the labelling of beef and beef products . \| bovine \| ('all', '0.08'), ('live', '0.07'), ('certain', '0.07'), ('the', '0.07'), ('farm', '0.05') ## Evaluation on downstream tasks Consider the experiments in the article "LEGAL-BERT: The Muppets straight out of Law School". Chalkidis et al., 2020, (https://aclanthology.org/2020.findings-emnlp.261) ## Author - Publication ``` @inproceedings{chalkidis-etal-2020-legal, title = "{LEGAL}-{BERT}: The Muppets straight out of Law School", author = "Chalkidis, Ilias and Fergadiotis, Manos and Malakasiotis, Prodromos and Aletras, Nikolaos and Androutsopoulos, Ion", booktitle = "Findings of the Association for Computational Linguistics: EMNLP 2020", month = nov, year = "2020", address = "Online", publisher = "Association for Computational Linguistics", doi = "10.18653/v1/2020.findings-emnlp.261", pages = "2898--2904" } ``` ## About Us [AUEB's Natural Language Processing Group](http://nlp.cs.aueb.gr) develops algorithms, models, and systems that allow computers to process and generate natural language texts. The group's current research interests include: * question answering systems for databases, ontologies, document collections, and the Web, especially biomedical question answering, * natural language generation from databases and ontologies, especially Semantic Web ontologies, text classification, including filtering spam and abusive content, * information extraction and opinion mining, including legal text analytics and sentiment analysis, * natural language processing tools for Greek, for example parsers and named-entity recognizers, machine learning in natural language processing, especially deep learning. The group is part of the Information Processing Laboratory of the Department of Informatics of the Athens University of Economics and Business. [Ilias Chalkidis](https://iliaschalkidis.github.io) on behalf of [AUEB's Natural Language Processing Group](http://nlp.cs.aueb.gr) \| Github: [@ilias.chalkidis](https://github.com/iliaschalkidis) \| Twitter: [@KiddoThe2B](https://twitter.com/KiddoThe2B) \|
nlpaueb/legal-bert-small-uncased	nlpaueb	2022-04-28T14:43:32Z	27,608	20	transformers	[ "transformers", "pytorch", "tf", "jax", "bert", "legal", "fill-mask", "en", "license:cc-by-sa-4.0", "endpoints_compatible", "region:us" ]	fill-mask	2022-03-02T23:29:05Z	--- language: en pipeline_tag: fill-mask license: cc-by-sa-4.0 thumbnail: https://i.ibb.co/p3kQ7Rw/Screenshot-2020-10-06-at-12-16-36-PM.png tags: - legal widget: - text: "The applicant submitted that her husband was subjected to treatment amounting to [MASK] whilst in the custody of police." --- # LEGAL-BERT: The Muppets straight out of Law School <img align="left" src="https://i.ibb.co/p3kQ7Rw/Screenshot-2020-10-06-at-12-16-36-PM.png" width="100"/> LEGAL-BERT is a family of BERT models for the legal domain, intended to assist legal NLP research, computational law, and legal technology applications. To pre-train the different variations of LEGAL-BERT, we collected 12 GB of diverse English legal text from several fields (e.g., legislation, court cases, contracts) scraped from publicly available resources. Sub-domain variants (CONTRACTS-, EURLEX-, ECHR-) and/or general LEGAL-BERT perform better than using BERT out of the box for domain-specific tasks.<br> This is the light-weight version of BERT-BASE (33% the size of BERT-BASE) pre-trained from scratch on legal data, which achieves comparable performance to larger models, while being much more efficient (approximately 4 times faster) with a smaller environmental footprint. <br/><br/> --- I. Chalkidis, M. Fergadiotis, P. Malakasiotis, N. Aletras and I. Androutsopoulos. "LEGAL-BERT: The Muppets straight out of Law School". In Findings of Empirical Methods in Natural Language Processing (EMNLP 2020) (Short Papers), to be held online, 2020. (https://aclanthology.org/2020.findings-emnlp.261) --- ## Pre-training corpora The pre-training corpora of LEGAL-BERT include: * 116,062 documents of EU legislation, publicly available from EURLEX (http://eur-lex.europa.eu), the repository of EU Law running under the EU Publication Office. * 61,826 documents of UK legislation, publicly available from the UK legislation portal (http://www.legislation.gov.uk). * 19,867 cases from the European Court of Justice (ECJ), also available from EURLEX. * 12,554 cases from HUDOC, the repository of the European Court of Human Rights (ECHR) (http://hudoc.echr.coe.int/eng). * 164,141 cases from various courts across the USA, hosted in the Case Law Access Project portal (https://case.law). * 76,366 US contracts from EDGAR, the database of US Securities and Exchange Commission (SECOM) (https://www.sec.gov/edgar.shtml). ## Pre-training details * We trained BERT using the official code provided in Google BERT's GitHub repository (https://github.com/google-research/bert). * We released a model similar to the English BERT-BASE model (12-layer, 768-hidden, 12-heads, 110M parameters). * We chose to follow the same training set-up: 1 million training steps with batches of 256 sequences of length 512 with an initial learning rate 1e-4. * We were able to use a single Google Cloud TPU v3-8 provided for free from [TensorFlow Research Cloud (TFRC)](https://www.tensorflow.org/tfrc), while also utilizing [GCP research credits](https://edu.google.com/programs/credits/research). Huge thanks to both Google programs for supporting us! ## Models list \| Model name \| Model Path \| Training corpora \| \| ------------------- \| ------------------------------------ \| ------------------- \| \| CONTRACTS-BERT-BASE \| `nlpaueb/bert-base-uncased-contracts` \| US contracts \| \| EURLEX-BERT-BASE \| `nlpaueb/bert-base-uncased-eurlex` \| EU legislation \| \| ECHR-BERT-BASE \| `nlpaueb/bert-base-uncased-echr` \| ECHR cases \| \| LEGAL-BERT-BASE * \| `nlpaueb/legal-bert-base-uncased` \| All \| \| LEGAL-BERT-SMALL \| `nlpaueb/legal-bert-small-uncased` \| All \| \* LEGAL-BERT-BASE is the model referred to as LEGAL-BERT-SC in Chalkidis et al. (2020); a model trained from scratch in the legal corpora mentioned below using a newly created vocabulary by a sentence-piece tokenizer trained on the very same corpora. \\ As many of you expressed interest in the LEGAL-BERT-FP models (those relying on the original BERT-BASE checkpoint), they have been released in Archive.org (https://archive.org/details/legal_bert_fp), as these models are secondary and possibly only interesting for those who aim to dig deeper in the open questions of Chalkidis et al. (2020). ## Load Pretrained Model ```python from transformers import AutoTokenizer, AutoModel tokenizer = AutoTokenizer.from_pretrained("nlpaueb/legal-bert-small-uncased") model = AutoModel.from_pretrained("nlpaueb/legal-bert-small-uncased") ``` ## Use LEGAL-BERT variants as Language Models \| Corpus \| Model \| Masked token \| Predictions \| \| --------------------------------- \| ---------------------------------- \| ------------ \| ------------ \| \| \| BERT-BASE-UNCASED \| \| (Contracts) \| This [MASK] Agreement is between General Motors and John Murray . \| employment \| ('new', '0.09'), ('current', '0.04'), ('proposed', '0.03'), ('marketing', '0.03'), ('joint', '0.02') \| (ECHR) \| The applicant submitted that her husband was subjected to treatment amounting to [MASK] whilst in the custody of Adana Security Directorate \| torture \| ('torture', '0.32'), ('rape', '0.22'), ('abuse', '0.14'), ('death', '0.04'), ('violence', '0.03') \| (EURLEX) \| Establishing a system for the identification and registration of [MASK] animals and regarding the labelling of beef and beef products . \| bovine \| ('farm', '0.25'), ('livestock', '0.08'), ('draft', '0.06'), ('domestic', '0.05'), ('wild', '0.05') \| \| CONTRACTS-BERT-BASE \| \| (Contracts) \| This [MASK] Agreement is between General Motors and John Murray . \| employment \| ('letter', '0.38'), ('dealer', '0.04'), ('employment', '0.03'), ('award', '0.03'), ('contribution', '0.02') \| (ECHR) \| The applicant submitted that her husband was subjected to treatment amounting to [MASK] whilst in the custody of Adana Security Directorate \| torture \| ('death', '0.39'), ('imprisonment', '0.07'), ('contempt', '0.05'), ('being', '0.03'), ('crime', '0.02') \| (EURLEX) \| Establishing a system for the identification and registration of [MASK] animals and regarding the labelling of beef and beef products . \| bovine \| (('domestic', '0.18'), ('laboratory', '0.07'), ('household', '0.06'), ('personal', '0.06'), ('the', '0.04') \| \| EURLEX-BERT-BASE \| \| (Contracts) \| This [MASK] Agreement is between General Motors and John Murray . \| employment \| ('supply', '0.11'), ('cooperation', '0.08'), ('service', '0.07'), ('licence', '0.07'), ('distribution', '0.05') \| (ECHR) \| The applicant submitted that her husband was subjected to treatment amounting to [MASK] whilst in the custody of Adana Security Directorate \| torture \| ('torture', '0.66'), ('death', '0.07'), ('imprisonment', '0.07'), ('murder', '0.04'), ('rape', '0.02') \| (EURLEX) \| Establishing a system for the identification and registration of [MASK] animals and regarding the labelling of beef and beef products . \| bovine \| ('live', '0.43'), ('pet', '0.28'), ('certain', '0.05'), ('fur', '0.03'), ('the', '0.02') \| \| ECHR-BERT-BASE \| \| (Contracts) \| This [MASK] Agreement is between General Motors and John Murray . \| employment \| ('second', '0.24'), ('latter', '0.10'), ('draft', '0.05'), ('bilateral', '0.05'), ('arbitration', '0.04') \| (ECHR) \| The applicant submitted that her husband was subjected to treatment amounting to [MASK] whilst in the custody of Adana Security Directorate \| torture \| ('torture', '0.99'), ('death', '0.01'), ('inhuman', '0.00'), ('beating', '0.00'), ('rape', '0.00') \| (EURLEX) \| Establishing a system for the identification and registration of [MASK] animals and regarding the labelling of beef and beef products . \| bovine \| ('pet', '0.17'), ('all', '0.12'), ('slaughtered', '0.10'), ('domestic', '0.07'), ('individual', '0.05') \| \| LEGAL-BERT-BASE \| \| (Contracts) \| This [MASK] Agreement is between General Motors and John Murray . \| employment \| ('settlement', '0.26'), ('letter', '0.23'), ('dealer', '0.04'), ('master', '0.02'), ('supplemental', '0.02') \| (ECHR) \| The applicant submitted that her husband was subjected to treatment amounting to [MASK] whilst in the custody of Adana Security Directorate \| torture \| ('torture', '1.00'), ('detention', '0.00'), ('arrest', '0.00'), ('rape', '0.00'), ('death', '0.00') \| (EURLEX) \| Establishing a system for the identification and registration of [MASK] animals and regarding the labelling of beef and beef products . \| bovine \| ('live', '0.67'), ('beef', '0.17'), ('farm', '0.03'), ('pet', '0.02'), ('dairy', '0.01') \| \| LEGAL-BERT-SMALL \| \| (Contracts) \| This [MASK] Agreement is between General Motors and John Murray . \| employment \| ('license', '0.09'), ('transition', '0.08'), ('settlement', '0.04'), ('consent', '0.03'), ('letter', '0.03') \| (ECHR) \| The applicant submitted that her husband was subjected to treatment amounting to [MASK] whilst in the custody of Adana Security Directorate \| torture \| ('torture', '0.59'), ('pain', '0.05'), ('ptsd', '0.05'), ('death', '0.02'), ('tuberculosis', '0.02') \| (EURLEX) \| Establishing a system for the identification and registration of [MASK] animals and regarding the labelling of beef and beef products . \| bovine \| ('all', '0.08'), ('live', '0.07'), ('certain', '0.07'), ('the', '0.07'), ('farm', '0.05') ## Evaluation on downstream tasks Consider the experiments in the article "LEGAL-BERT: The Muppets straight out of Law School". Chalkidis et al., 2020, (https://aclanthology.org/2020.findings-emnlp.261) ## Author - Publication ``` @inproceedings{chalkidis-etal-2020-legal, title = "{LEGAL}-{BERT}: The Muppets straight out of Law School", author = "Chalkidis, Ilias and Fergadiotis, Manos and Malakasiotis, Prodromos and Aletras, Nikolaos and Androutsopoulos, Ion", booktitle = "Findings of the Association for Computational Linguistics: EMNLP 2020", month = nov, year = "2020", address = "Online", publisher = "Association for Computational Linguistics", doi = "10.18653/v1/2020.findings-emnlp.261", pages = "2898--2904" } ``` ## About Us [AUEB's Natural Language Processing Group](http://nlp.cs.aueb.gr) develops algorithms, models, and systems that allow computers to process and generate natural language texts. The group's current research interests include: * question answering systems for databases, ontologies, document collections, and the Web, especially biomedical question answering, * natural language generation from databases and ontologies, especially Semantic Web ontologies, text classification, including filtering spam and abusive content, * information extraction and opinion mining, including legal text analytics and sentiment analysis, * natural language processing tools for Greek, for example parsers and named-entity recognizers, machine learning in natural language processing, especially deep learning. The group is part of the Information Processing Laboratory of the Department of Informatics of the Athens University of Economics and Business. [Ilias Chalkidis](https://iliaschalkidis.github.io) on behalf of [AUEB's Natural Language Processing Group](http://nlp.cs.aueb.gr) \| Github: [@ilias.chalkidis](https://github.com/iliaschalkidis) \| Twitter: [@KiddoThe2B](https://twitter.com/KiddoThe2B) \|
nlpaueb/legal-bert-base-uncased	nlpaueb	2022-04-28T14:42:50Z	525,138	197	transformers	[ "transformers", "pytorch", "tf", "jax", "bert", "pretraining", "legal", "fill-mask", "en", "license:cc-by-sa-4.0", "endpoints_compatible", "region:us" ]	fill-mask	2022-03-02T23:29:05Z	--- language: en pipeline_tag: fill-mask license: cc-by-sa-4.0 thumbnail: https://i.ibb.co/p3kQ7Rw/Screenshot-2020-10-06-at-12-16-36-PM.png tags: - legal widget: - text: "The applicant submitted that her husband was subjected to treatment amounting to [MASK] whilst in the custody of police." --- # LEGAL-BERT: The Muppets straight out of Law School <img align="left" src="https://i.ibb.co/p3kQ7Rw/Screenshot-2020-10-06-at-12-16-36-PM.png" width="100"/> LEGAL-BERT is a family of BERT models for the legal domain, intended to assist legal NLP research, computational law, and legal technology applications. To pre-train the different variations of LEGAL-BERT, we collected 12 GB of diverse English legal text from several fields (e.g., legislation, court cases, contracts) scraped from publicly available resources. Sub-domain variants (CONTRACTS-, EURLEX-, ECHR-) and/or general LEGAL-BERT perform better than using BERT out of the box for domain-specific tasks. A light-weight model (33% the size of BERT-BASE) pre-trained from scratch on legal data with competitive performance is also available. <br/><br/> --- I. Chalkidis, M. Fergadiotis, P. Malakasiotis, N. Aletras and I. Androutsopoulos. "LEGAL-BERT: The Muppets straight out of Law School". In Findings of Empirical Methods in Natural Language Processing (EMNLP 2020) (Short Papers), to be held online, 2020. (https://aclanthology.org/2020.findings-emnlp.261) --- ## Pre-training corpora The pre-training corpora of LEGAL-BERT include: * 116,062 documents of EU legislation, publicly available from EURLEX (http://eur-lex.europa.eu), the repository of EU Law running under the EU Publication Office. * 61,826 documents of UK legislation, publicly available from the UK legislation portal (http://www.legislation.gov.uk). * 19,867 cases from the European Court of Justice (ECJ), also available from EURLEX. * 12,554 cases from HUDOC, the repository of the European Court of Human Rights (ECHR) (http://hudoc.echr.coe.int/eng). * 164,141 cases from various courts across the USA, hosted in the Case Law Access Project portal (https://case.law). * 76,366 US contracts from EDGAR, the database of US Securities and Exchange Commission (SECOM) (https://www.sec.gov/edgar.shtml). ## Pre-training details * We trained BERT using the official code provided in Google BERT's GitHub repository (https://github.com/google-research/bert). * We released a model similar to the English BERT-BASE model (12-layer, 768-hidden, 12-heads, 110M parameters). * We chose to follow the same training set-up: 1 million training steps with batches of 256 sequences of length 512 with an initial learning rate 1e-4. * We were able to use a single Google Cloud TPU v3-8 provided for free from [TensorFlow Research Cloud (TFRC)](https://www.tensorflow.org/tfrc), while also utilizing [GCP research credits](https://edu.google.com/programs/credits/research). Huge thanks to both Google programs for supporting us! * Part of LEGAL-BERT is a light-weight model pre-trained from scratch on legal data, which achieves comparable performance to larger models, while being much more efficient (approximately 4 times faster) with a smaller environmental footprint. ## Models list \| Model name \| Model Path \| Training corpora \| \| ------------------- \| ------------------------------------ \| ------------------- \| \| CONTRACTS-BERT-BASE \| `nlpaueb/bert-base-uncased-contracts` \| US contracts \| \| EURLEX-BERT-BASE \| `nlpaueb/bert-base-uncased-eurlex` \| EU legislation \| \| ECHR-BERT-BASE \| `nlpaueb/bert-base-uncased-echr` \| ECHR cases \| \| LEGAL-BERT-BASE * \| `nlpaueb/legal-bert-base-uncased` \| All \| \| LEGAL-BERT-SMALL \| `nlpaueb/legal-bert-small-uncased` \| All \| \* LEGAL-BERT-BASE is the model referred to as LEGAL-BERT-SC in Chalkidis et al. (2020); a model trained from scratch in the legal corpora mentioned below using a newly created vocabulary by a sentence-piece tokenizer trained on the very same corpora. \\ As many of you expressed interest in the LEGAL-BERT-FP models (those relying on the original BERT-BASE checkpoint), they have been released in Archive.org (https://archive.org/details/legal_bert_fp), as these models are secondary and possibly only interesting for those who aim to dig deeper in the open questions of Chalkidis et al. (2020). ## Load Pretrained Model ```python from transformers import AutoTokenizer, AutoModel tokenizer = AutoTokenizer.from_pretrained("nlpaueb/legal-bert-base-uncased") model = AutoModel.from_pretrained("nlpaueb/legal-bert-base-uncased") ``` ## Use LEGAL-BERT variants as Language Models \| Corpus \| Model \| Masked token \| Predictions \| \| --------------------------------- \| ---------------------------------- \| ------------ \| ------------ \| \| \| BERT-BASE-UNCASED \| \| (Contracts) \| This [MASK] Agreement is between General Motors and John Murray . \| employment \| ('new', '0.09'), ('current', '0.04'), ('proposed', '0.03'), ('marketing', '0.03'), ('joint', '0.02') \| (ECHR) \| The applicant submitted that her husband was subjected to treatment amounting to [MASK] whilst in the custody of Adana Security Directorate \| torture \| ('torture', '0.32'), ('rape', '0.22'), ('abuse', '0.14'), ('death', '0.04'), ('violence', '0.03') \| (EURLEX) \| Establishing a system for the identification and registration of [MASK] animals and regarding the labelling of beef and beef products . \| bovine \| ('farm', '0.25'), ('livestock', '0.08'), ('draft', '0.06'), ('domestic', '0.05'), ('wild', '0.05') \| \| CONTRACTS-BERT-BASE \| \| (Contracts) \| This [MASK] Agreement is between General Motors and John Murray . \| employment \| ('letter', '0.38'), ('dealer', '0.04'), ('employment', '0.03'), ('award', '0.03'), ('contribution', '0.02') \| (ECHR) \| The applicant submitted that her husband was subjected to treatment amounting to [MASK] whilst in the custody of Adana Security Directorate \| torture \| ('death', '0.39'), ('imprisonment', '0.07'), ('contempt', '0.05'), ('being', '0.03'), ('crime', '0.02') \| (EURLEX) \| Establishing a system for the identification and registration of [MASK] animals and regarding the labelling of beef and beef products . \| bovine \| (('domestic', '0.18'), ('laboratory', '0.07'), ('household', '0.06'), ('personal', '0.06'), ('the', '0.04') \| \| EURLEX-BERT-BASE \| \| (Contracts) \| This [MASK] Agreement is between General Motors and John Murray . \| employment \| ('supply', '0.11'), ('cooperation', '0.08'), ('service', '0.07'), ('licence', '0.07'), ('distribution', '0.05') \| (ECHR) \| The applicant submitted that her husband was subjected to treatment amounting to [MASK] whilst in the custody of Adana Security Directorate \| torture \| ('torture', '0.66'), ('death', '0.07'), ('imprisonment', '0.07'), ('murder', '0.04'), ('rape', '0.02') \| (EURLEX) \| Establishing a system for the identification and registration of [MASK] animals and regarding the labelling of beef and beef products . \| bovine \| ('live', '0.43'), ('pet', '0.28'), ('certain', '0.05'), ('fur', '0.03'), ('the', '0.02') \| \| ECHR-BERT-BASE \| \| (Contracts) \| This [MASK] Agreement is between General Motors and John Murray . \| employment \| ('second', '0.24'), ('latter', '0.10'), ('draft', '0.05'), ('bilateral', '0.05'), ('arbitration', '0.04') \| (ECHR) \| The applicant submitted that her husband was subjected to treatment amounting to [MASK] whilst in the custody of Adana Security Directorate \| torture \| ('torture', '0.99'), ('death', '0.01'), ('inhuman', '0.00'), ('beating', '0.00'), ('rape', '0.00') \| (EURLEX) \| Establishing a system for the identification and registration of [MASK] animals and regarding the labelling of beef and beef products . \| bovine \| ('pet', '0.17'), ('all', '0.12'), ('slaughtered', '0.10'), ('domestic', '0.07'), ('individual', '0.05') \| \| LEGAL-BERT-BASE \| \| (Contracts) \| This [MASK] Agreement is between General Motors and John Murray . \| employment \| ('settlement', '0.26'), ('letter', '0.23'), ('dealer', '0.04'), ('master', '0.02'), ('supplemental', '0.02') \| (ECHR) \| The applicant submitted that her husband was subjected to treatment amounting to [MASK] whilst in the custody of Adana Security Directorate \| torture \| ('torture', '1.00'), ('detention', '0.00'), ('arrest', '0.00'), ('rape', '0.00'), ('death', '0.00') \| (EURLEX) \| Establishing a system for the identification and registration of [MASK] animals and regarding the labelling of beef and beef products . \| bovine \| ('live', '0.67'), ('beef', '0.17'), ('farm', '0.03'), ('pet', '0.02'), ('dairy', '0.01') \| \| LEGAL-BERT-SMALL \| \| (Contracts) \| This [MASK] Agreement is between General Motors and John Murray . \| employment \| ('license', '0.09'), ('transition', '0.08'), ('settlement', '0.04'), ('consent', '0.03'), ('letter', '0.03') \| (ECHR) \| The applicant submitted that her husband was subjected to treatment amounting to [MASK] whilst in the custody of Adana Security Directorate \| torture \| ('torture', '0.59'), ('pain', '0.05'), ('ptsd', '0.05'), ('death', '0.02'), ('tuberculosis', '0.02') \| (EURLEX) \| Establishing a system for the identification and registration of [MASK] animals and regarding the labelling of beef and beef products . \| bovine \| ('all', '0.08'), ('live', '0.07'), ('certain', '0.07'), ('the', '0.07'), ('farm', '0.05') ## Evaluation on downstream tasks Consider the experiments in the article "LEGAL-BERT: The Muppets straight out of Law School". Chalkidis et al., 2020, (https://aclanthology.org/2020.findings-emnlp.261) ## Author - Publication ``` @inproceedings{chalkidis-etal-2020-legal, title = "{LEGAL}-{BERT}: The Muppets straight out of Law School", author = "Chalkidis, Ilias and Fergadiotis, Manos and Malakasiotis, Prodromos and Aletras, Nikolaos and Androutsopoulos, Ion", booktitle = "Findings of the Association for Computational Linguistics: EMNLP 2020", month = nov, year = "2020", address = "Online", publisher = "Association for Computational Linguistics", doi = "10.18653/v1/2020.findings-emnlp.261", pages = "2898--2904" } ``` ## About Us [AUEB's Natural Language Processing Group](http://nlp.cs.aueb.gr) develops algorithms, models, and systems that allow computers to process and generate natural language texts. The group's current research interests include: * question answering systems for databases, ontologies, document collections, and the Web, especially biomedical question answering, * natural language generation from databases and ontologies, especially Semantic Web ontologies, text classification, including filtering spam and abusive content, * information extraction and opinion mining, including legal text analytics and sentiment analysis, * natural language processing tools for Greek, for example parsers and named-entity recognizers, machine learning in natural language processing, especially deep learning. The group is part of the Information Processing Laboratory of the Department of Informatics of the Athens University of Economics and Business. [Ilias Chalkidis](https://iliaschalkidis.github.io) on behalf of [AUEB's Natural Language Processing Group](http://nlp.cs.aueb.gr) \| Github: [@ilias.chalkidis](https://github.com/iliaschalkidis) \| Twitter: [@KiddoThe2B](https://twitter.com/KiddoThe2B) \|
horychtom/czech_media_bias_classifier	horychtom	2022-04-28T13:51:18Z	4	2	transformers	[ "transformers", "pytorch", "bert", "text-classification", "Czech", "cs", "autotrain_compatible", "region:us" ]	text-classification	2022-04-04T09:04:34Z	--- inference: false language: "cs" tags: - Czech --- ## Czech Media Bias Classifier A FERNET-C5 model fine-tuned to perform binary classification task on czech media bias detection.
anton-l/xtreme_s_xlsr_300m_fleurs_asr_en_us	anton-l	2022-04-28T12:39:54Z	11	0	transformers	[ "transformers", "pytorch", "tensorboard", "wav2vec2", "automatic-speech-recognition", "fleurs-asr", "google/xtreme_s", "generated_from_trainer", "dataset:google/xtreme_s", "license:apache-2.0", "endpoints_compatible", "region:us" ]	automatic-speech-recognition	2022-04-28T10:45:25Z	--- language: - en_us license: apache-2.0 tags: - fleurs-asr - google/xtreme_s - generated_from_trainer datasets: - google/xtreme_s model-index: - name: xtreme_s_xlsr_300m_fleurs_asr_en_us results: [] --- <!-- This model card has been generated automatically according to the information the Trainer had access to. You should probably proofread and complete it, then remove this comment. --> # xtreme_s_xlsr_300m_fleurs_asr_en_us This model is a fine-tuned version of [facebook/wav2vec2-xls-r-300m](https://huggingface.co/facebook/wav2vec2-xls-r-300m) on the GOOGLE/XTREME_S - FLEURS.EN_US dataset. It achieves the following results on the evaluation set: - Cer: 0.1356 - Loss: 0.5599 - Wer: 0.3148 - Predict Samples: 647 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 0.0003 - train_batch_size: 8 - eval_batch_size: 1 - seed: 42 - distributed_type: multi-GPU - num_devices: 8 - total_train_batch_size: 64 - total_eval_batch_size: 8 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - lr_scheduler_warmup_steps: 200 - num_epochs: 30.0 - mixed_precision_training: Native AMP ### Training results \| Training Loss \| Epoch \| Step \| Validation Loss \| Wer \| Cer \| \|:-------------:\|:-----:\|:----:\|:---------------:\|:------:\|:------:\| \| 2.8769 \| 5.0 \| 200 \| 2.8871 \| 1.0 \| 0.9878 \| \| 0.2458 \| 10.0 \| 400 \| 0.5570 \| 0.4899 \| 0.1951 \| \| 0.0762 \| 15.0 \| 600 \| 0.5213 \| 0.3727 \| 0.1562 \| \| 0.0334 \| 20.0 \| 800 \| 0.5742 \| 0.3666 \| 0.1543 \| \| 0.0244 \| 25.0 \| 1000 \| 0.5907 \| 0.3546 \| 0.1499 \| \| 0.0143 \| 30.0 \| 1200 \| 0.5961 \| 0.3460 \| 0.1469 \| ### Framework versions - Transformers 4.18.0.dev0 - Pytorch 1.10.1+cu111 - Datasets 1.18.4.dev0 - Tokenizers 0.11.6
anton-l/xtreme_s_xlsr_300m_fleurs_asr_western_european	anton-l	2022-04-28T09:56:22Z	23	1	transformers	[ "transformers", "pytorch", "tensorboard", "wav2vec2", "automatic-speech-recognition", "fleurs-asr", "google/xtreme_s", "generated_from_trainer", "all", "dataset:google/xtreme_s", "license:apache-2.0", "endpoints_compatible", "region:us" ]	automatic-speech-recognition	2022-04-27T10:27:11Z	--- language: - all license: apache-2.0 tags: - fleurs-asr - google/xtreme_s - generated_from_trainer datasets: - google/xtreme_s model-index: - name: xtreme_s_xlsr_300m_fleurs_asr_western_european results: [] --- <!-- This model card has been generated automatically according to the information the Trainer had access to. You should probably proofread and complete it, then remove this comment. --> # xtreme_s_xlsr_300m_fleurs_asr_western_european This model is a fine-tuned version of [facebook/wav2vec2-xls-r-300m](https://huggingface.co/facebook/wav2vec2-xls-r-300m) on the GOOGLE/XTREME_S - FLEURS.ALL dataset. It achieves the following results on the evaluation set: - Cer: 0.2484 - Cer Ast Es: 0.1598 - Cer Bs Ba: 0.1749 - Cer Ca Es: 0.1655 - Cer Cy Gb: 0.2280 - Cer Da Dk: 0.3616 - Cer De De: 0.1287 - Cer El Gr: 0.6020 - Cer En Us: 0.1938 - Cer Es 419: 0.1288 - Cer Fi Fi: 0.2050 - Cer Fr Fr: 0.1811 - Cer Ga Ie: 0.4474 - Cer Gl Es: 0.1324 - Cer Hr Hr: 0.1555 - Cer Hu Hu: 0.3911 - Cer Is Is: 0.4646 - Cer It It: 0.1283 - Cer Kea Cv: 0.1818 - Cer Lb Lu: 0.2594 - Cer Mt Mt: 0.3628 - Cer Nb No: 0.2254 - Cer Nl Nl: 0.1790 - Cer Oci Fr: 0.2159 - Cer Pt Br: 0.2275 - Cer Sv Se: 0.3092 - Loss: 1.3089 - Loss Ast Es: 0.7715 - Loss Bs Ba: 0.7378 - Loss Ca Es: 0.7868 - Loss Cy Gb: 1.1441 - Loss Da Dk: 1.9130 - Loss De De: 0.5391 - Loss El Gr: 3.4904 - Loss En Us: 0.9632 - Loss Es 419: 0.6186 - Loss Fi Fi: 0.8953 - Loss Fr Fr: 0.9076 - Loss Ga Ie: 3.0217 - Loss Gl Es: 0.5788 - Loss Hr Hr: 0.6462 - Loss Hu Hu: 1.9029 - Loss Is Is: 2.6551 - Loss It It: 0.6052 - Loss Kea Cv: 0.9107 - Loss Lb Lu: 1.3705 - Loss Mt Mt: 2.3651 - Loss Nb No: 1.1518 - Loss Nl Nl: 0.8490 - Loss Oci Fr: 1.1421 - Loss Pt Br: 1.1641 - Loss Sv Se: 1.5910 - Wer: 0.6451 - Wer Ast Es: 0.4654 - Wer Bs Ba: 0.5443 - Wer Ca Es: 0.4979 - Wer Cy Gb: 0.5962 - Wer Da Dk: 0.8455 - Wer De De: 0.4221 - Wer El Gr: 0.9805 - Wer En Us: 0.4556 - Wer Es 419: 0.3928 - Wer Fi Fi: 0.8116 - Wer Fr Fr: 0.4690 - Wer Ga Ie: 0.8519 - Wer Gl Es: 0.4245 - Wer Hr Hr: 0.4895 - Wer Hu Hu: 0.9099 - Wer Is Is: 0.9960 - Wer It It: 0.4415 - Wer Kea Cv: 0.5202 - Wer Lb Lu: 0.7225 - Wer Mt Mt: 1.0096 - Wer Nb No: 0.6541 - Wer Nl Nl: 0.5257 - Wer Oci Fr: 0.5770 - Wer Pt Br: 0.6685 - Wer Sv Se: 0.8546 - Predict Samples: 20043 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 0.0003 - train_batch_size: 8 - eval_batch_size: 1 - seed: 42 - distributed_type: multi-GPU - num_devices: 8 - total_train_batch_size: 64 - total_eval_batch_size: 8 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - lr_scheduler_warmup_steps: 1000 - num_epochs: 20.0 - mixed_precision_training: Native AMP ### Training results \| Training Loss \| Epoch \| Step \| Validation Loss \| Wer \| Cer \| \|:-------------:\|:-----:\|:-----:\|:---------------:\|:------:\|:------:\| \| 3.1411 \| 0.49 \| 500 \| 3.1673 \| 1.0 \| 1.0 \| \| 0.6397 \| 0.97 \| 1000 \| 0.9039 \| 0.7171 \| 0.2862 \| \| 0.4033 \| 1.46 \| 1500 \| 0.8914 \| 0.6862 \| 0.2763 \| \| 0.3473 \| 1.94 \| 2000 \| 0.8017 \| 0.6505 \| 0.2536 \| \| 0.3143 \| 2.43 \| 2500 \| 0.8568 \| 0.6566 \| 0.2627 \| \| 0.3004 \| 2.91 \| 3000 \| 0.8898 \| 0.6640 \| 0.2686 \| \| 0.282 \| 3.4 \| 3500 \| 0.8489 \| 0.6637 \| 0.2571 \| \| 0.2489 \| 3.88 \| 4000 \| 0.8955 \| 0.6744 \| 0.2691 \| \| 0.1706 \| 4.37 \| 4500 \| 0.9190 \| 0.6788 \| 0.2688 \| \| 0.3336 \| 4.85 \| 5000 \| 0.8915 \| 0.6594 \| 0.2572 \| \| 0.1426 \| 5.34 \| 5500 \| 0.9501 \| 0.6784 \| 0.2686 \| \| 0.2301 \| 5.83 \| 6000 \| 1.0217 \| 0.6719 \| 0.2735 \| \| 0.1325 \| 6.31 \| 6500 \| 0.9578 \| 0.6691 \| 0.2655 \| \| 0.1145 \| 6.8 \| 7000 \| 0.9129 \| 0.6680 \| 0.2593 \| \| 0.1202 \| 7.28 \| 7500 \| 0.9646 \| 0.6749 \| 0.2619 \| \| 0.143 \| 7.77 \| 8000 \| 0.9200 \| 0.6554 \| 0.2554 \| \| 0.1012 \| 8.25 \| 8500 \| 0.9553 \| 0.6787 \| 0.2628 \| \| 0.1018 \| 8.74 \| 9000 \| 0.9455 \| 0.6445 \| 0.2511 \| \| 0.1148 \| 9.22 \| 9500 \| 1.0206 \| 0.6725 \| 0.2629 \| \| 0.0794 \| 9.71 \| 10000 \| 0.9305 \| 0.6547 \| 0.2526 \| \| 0.2891 \| 10.19 \| 10500 \| 1.0424 \| 0.6709 \| 0.2570 \| \| 0.1665 \| 10.68 \| 11000 \| 0.9760 \| 0.6596 \| 0.2507 \| \| 0.1956 \| 11.17 \| 11500 \| 0.9549 \| 0.6340 \| 0.2440 \| \| 0.0828 \| 11.65 \| 12000 \| 0.9598 \| 0.6403 \| 0.2460 \| \| 0.059 \| 12.14 \| 12500 \| 0.9972 \| 0.6574 \| 0.2531 \| \| 0.0505 \| 12.62 \| 13000 \| 0.9836 \| 0.6534 \| 0.2525 \| \| 0.0336 \| 13.11 \| 13500 \| 1.0619 \| 0.6564 \| 0.2519 \| \| 0.0435 \| 13.59 \| 14000 \| 1.0844 \| 0.6480 \| 0.2543 \| \| 0.0216 \| 14.08 \| 14500 \| 1.1084 \| 0.6512 \| 0.2521 \| \| 0.0265 \| 14.56 \| 15000 \| 1.1152 \| 0.6607 \| 0.2563 \| \| 0.0975 \| 15.05 \| 15500 \| 1.1060 \| 0.6456 \| 0.2471 \| \| 0.1396 \| 15.53 \| 16000 \| 1.1100 \| 0.6337 \| 0.2418 \| \| 0.0701 \| 16.02 \| 16500 \| 1.1731 \| 0.6309 \| 0.2415 \| \| 0.1171 \| 16.5 \| 17000 \| 1.1302 \| 0.6315 \| 0.2396 \| \| 0.0778 \| 16.99 \| 17500 \| 1.1485 \| 0.6379 \| 0.2447 \| \| 0.0642 \| 17.48 \| 18000 \| 1.2009 \| 0.6400 \| 0.2464 \| \| 0.0322 \| 17.96 \| 18500 \| 1.2028 \| 0.6357 \| 0.2425 \| \| 0.031 \| 18.45 \| 19000 \| 1.2381 \| 0.6285 \| 0.2416 \| \| 0.0579 \| 18.93 \| 19500 \| 1.2299 \| 0.6265 \| 0.2409 \| \| 0.0628 \| 19.42 \| 20000 \| 1.2582 \| 0.6277 \| 0.2395 \| \| 0.074 \| 19.9 \| 20500 \| 1.2572 \| 0.6278 \| 0.2394 \| ### Framework versions - Transformers 4.18.0.dev0 - Pytorch 1.10.1+cu111 - Datasets 1.18.4.dev0 - Tokenizers 0.11.6
daveni/twitter-xlm-roberta-emotion-es	daveni	2022-04-28T09:49:06Z	602	21	transformers	[ "transformers", "pytorch", "xlm-roberta", "text-classification", "Emotion Analysis", "es", "autotrain_compatible", "endpoints_compatible", "region:us" ]	text-classification	2022-03-02T23:29:05Z	--- language: - es tags: - Emotion Analysis --- Note: This model & model card are based on the [finetuned XLM-T for Sentiment Analysis](https://huggingface.co/cardiffnlp/twitter-xlm-roberta-base-sentiment) # twitter-XLM-roBERTa-base for Emotion Analysis This is a XLM-roBERTa-base model trained on ~198M tweets and finetuned for emotion analysis on Spanish language. This model was presented to EmoEvalEs competition, part of [IberLEF 2021 Conference](https://sites.google.com/view/iberlef2021/), where the proposed task was the classification of Spanish tweets between seven different classes: anger, disgust, fear, joy, sadness, surprise, and other. We achieved the first position in the competition with a macro-averaged F1 score of 71.70%. - [Our code for EmoEvalEs submission](https://github.com/gsi-upm/emoevales-iberlef2021). - [EmoEvalEs Dataset](https://github.com/pendrag/EmoEvalEs) ## Example Pipeline with a [Tweet from @JaSantaolalla](https://twitter.com/JaSantaolalla/status/1398383243645177860) ```python from transformers import pipeline model_path = "daveni/twitter-xlm-roberta-emotion-es" emotion_analysis = pipeline("text-classification", framework="pt", model=model_path, tokenizer=model_path) emotion_analysis("Einstein dijo: Solo hay dos cosas infinitas, el universo y los pinches anuncios de bitcoin en Twitter. Paren ya carajo aaaaaaghhgggghhh me quiero murir") ``` ``` [{'label': 'anger', 'score': 0.48307016491889954}] ``` ## Full classification example ```python from transformers import AutoModelForSequenceClassification from transformers import AutoTokenizer, AutoConfig import numpy as np from scipy.special import softmax # Preprocess text (username and link placeholders) def preprocess(text): new_text = [] for t in text.split(" "): t = '@user' if t.startswith('@') and len(t) > 1 else t t = 'http' if t.startswith('http') else t new_text.append(t) return " ".join(new_text) model_path = "daveni/twitter-xlm-roberta-emotion-es" tokenizer = AutoTokenizer.from_pretrained(model_path ) config = AutoConfig.from_pretrained(model_path ) # PT model = AutoModelForSequenceClassification.from_pretrained(model_path ) text = "Se ha quedao bonito día para publicar vídeo, ¿no? Hoy del tema más diferente que hemos tocado en el canal." text = preprocess(text) print(text) encoded_input = tokenizer(text, return_tensors='pt') output = model(*encoded_input) scores = output[0][0].detach().numpy() scores = softmax(scores) # Print labels and scores ranking = np.argsort(scores) ranking = ranking[::-1] for i in range(scores.shape[0]): l = config.id2label[ranking[i]] s = scores[ranking[i]] print(f"{i+1}) {l} {np.round(float(s), 4)}") ``` Output: ``` Se ha quedao bonito día para publicar vídeo, ¿no? Hoy del tema más diferente que hemos tocado en el canal. 1) joy 0.7887 2) others 0.1679 3) surprise 0.0152 4) sadness 0.0145 5) anger 0.0077 6) disgust 0.0033 7) fear 0.0027 ``` #### Limitations and bias - The dataset we used for finetuning was unbalanced, where almost half of the records belonged to the other* class so there might be bias towards this class. ## Training data Pretrained weights were left identical to the original model released by [cardiffnlp](https://huggingface.co/cardiffnlp/twitter-xlm-roberta-base). We used the [EmoEvalEs Dataset](https://github.com/pendrag/EmoEvalEs) for finetuning. ### BibTeX entry and citation info ```bibtex @inproceedings{vera2021gsi, title={GSI-UPM at IberLEF2021: Emotion Analysis of Spanish Tweets by Fine-tuning the XLM-RoBERTa Language Model}, author={Vera, D and Araque, O and Iglesias, CA}, booktitle={Proceedings of the Iberian Languages Evaluation Forum (IberLEF 2021). CEUR Workshop Proceedings, CEUR-WS, M{\'a}laga, Spain}, year={2021} } ```
bdickson/bert-base-uncased-finetuned-squad	bdickson	2022-04-28T07:30:32Z	3	0	transformers	[ "transformers", "pytorch", "tensorboard", "bert", "question-answering", "generated_from_trainer", "dataset:squad", "license:apache-2.0", "endpoints_compatible", "region:us" ]	question-answering	2022-04-28T00:58:17Z	--- license: apache-2.0 tags: - generated_from_trainer datasets: - squad model-index: - name: bert-base-uncased-finetuned-squad results: [] --- <!-- This model card has been generated automatically according to the information the Trainer had access to. You should probably proofread and complete it, then remove this comment. --> # bert-base-uncased-finetuned-squad This model is a fine-tuned version of [bert-base-uncased](https://huggingface.co/bert-base-uncased) on the squad dataset. It achieves the following results on the evaluation set: - eval_loss: 1.1240 - eval_runtime: 262.7193 - eval_samples_per_second: 41.048 - eval_steps_per_second: 2.565 - epoch: 3.0 - step: 16599 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 2e-05 - train_batch_size: 16 - eval_batch_size: 16 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - num_epochs: 5 ### Framework versions - Transformers 4.18.0 - Pytorch 1.11.0+cu113 - Datasets 2.1.0 - Tokenizers 0.12.1
Lilya/distilbert-base-uncased-finetuned-ner-TRANS	Lilya	2022-04-28T07:00:58Z	9	0	transformers	[ "transformers", "pytorch", "tensorboard", "distilbert", "token-classification", "generated_from_trainer", "license:apache-2.0", "autotrain_compatible", "endpoints_compatible", "region:us" ]	token-classification	2022-04-27T11:44:59Z	--- license: apache-2.0 tags: - generated_from_trainer metrics: - precision - recall - f1 - accuracy model-index: - name: distilbert-base-uncased-finetuned-ner-TRANS results: [] --- <!-- This model card has been generated automatically according to the information the Trainer had access to. You should probably proofread and complete it, then remove this comment. --> # distilbert-base-uncased-finetuned-ner-TRANS This model is a fine-tuned version of [distilbert-base-uncased](https://huggingface.co/distilbert-base-uncased) on the None dataset. It achieves the following results on the evaluation set: - Loss: 0.1053 - Precision: 0.7911 - Recall: 0.8114 - F1: 0.8011 - Accuracy: 0.9815 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 2e-05 - train_batch_size: 16 - eval_batch_size: 16 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - num_epochs: 12 ### Training results \| Training Loss \| Epoch \| Step \| Validation Loss \| Precision \| Recall \| F1 \| Accuracy \| \|:-------------:\|:-----:\|:-----:\|:---------------:\|:---------:\|:------:\|:------:\|:--------:\| \| 0.077 \| 1.0 \| 3762 \| 0.0724 \| 0.7096 \| 0.7472 \| 0.7279 \| 0.9741 \| \| 0.0538 \| 2.0 \| 7524 \| 0.0652 \| 0.7308 \| 0.7687 \| 0.7493 \| 0.9766 \| \| 0.0412 \| 3.0 \| 11286 \| 0.0643 \| 0.7672 \| 0.7875 \| 0.7772 \| 0.9788 \| \| 0.0315 \| 4.0 \| 15048 \| 0.0735 \| 0.7646 \| 0.7966 \| 0.7803 \| 0.9793 \| \| 0.0249 \| 5.0 \| 18810 \| 0.0772 \| 0.7805 \| 0.7981 \| 0.7892 \| 0.9801 \| \| 0.0213 \| 6.0 \| 22572 \| 0.0783 \| 0.7829 \| 0.8063 \| 0.7944 \| 0.9805 \| \| 0.0187 \| 7.0 \| 26334 \| 0.0858 \| 0.7821 \| 0.8010 \| 0.7914 \| 0.9809 \| \| 0.0157 \| 8.0 \| 30096 \| 0.0860 \| 0.7837 \| 0.8120 \| 0.7976 \| 0.9812 \| \| 0.0122 \| 9.0 \| 33858 \| 0.0963 \| 0.7857 \| 0.8129 \| 0.7990 \| 0.9813 \| \| 0.0107 \| 10.0 \| 37620 \| 0.0993 \| 0.7934 \| 0.8089 \| 0.8010 \| 0.9812 \| \| 0.0091 \| 11.0 \| 41382 \| 0.1031 \| 0.7882 \| 0.8123 \| 0.8001 \| 0.9814 \| \| 0.0083 \| 12.0 \| 45144 \| 0.1053 \| 0.7911 \| 0.8114 \| 0.8011 \| 0.9815 \| ### Framework versions - Transformers 4.15.0 - Pytorch 1.10.1 - Datasets 2.0.0 - Tokenizers 0.10.3
OWG/resnet-50	OWG	2022-04-28T06:54:33Z	0	1	null	[ "onnx", "ResNet-50", "en", "arxiv:1512.03385", "region:us" ]	null	2022-04-28T06:22:56Z	--- language: - en tags: - ResNet-50 --- # ResNet-50 ## Model Description ResNet-50 model from [Deep Residual Learning for Image Recognition](https://arxiv.org/abs/1512.03385) paper. ## Original implementation Follow [this link](https://huggingface.co/microsoft/resnet-50) to see the original implementation. # How to use You can use the `base` model that returns `last_hidden_state`. ```python from transformers import AutoFeatureExtractor from onnxruntime import InferenceSession from datasets import load_dataset # load image dataset = load_dataset("huggingface/cats-image") image = dataset["test"]["image"][0] # load model feature_extractor = AutoFeatureExtractor.from_pretrained("microsoft/resnet-50") session = InferenceSession("onnx/model.onnx") # ONNX Runtime expects NumPy arrays as input inputs = feature_extractor(image, return_tensors="np") outputs = session.run(output_names=["last_hidden_state"], input_feed=dict(inputs)) ``` Or you can use the model with classification head that returns `logits`. ```python from transformers import AutoFeatureExtractor from onnxruntime import InferenceSession from datasets import load_dataset # load image dataset = load_dataset("huggingface/cats-image") image = dataset["test"]["image"][0] # load model feature_extractor = AutoFeatureExtractor.from_pretrained("microsoft/resnet-50") session = InferenceSession("onnx/model_cls.onnx") # ONNX Runtime expects NumPy arrays as input inputs = feature_extractor(image, return_tensors="np") outputs = session.run(output_names=["logits"], input_feed=dict(inputs)) ```
bdickson/electra-small-discriminator-finetuned-squad-finetuned-squad	bdickson	2022-04-28T06:40:32Z	3	0	transformers	[ "transformers", "pytorch", "electra", "question-answering", "generated_from_trainer", "dataset:squad", "endpoints_compatible", "region:us" ]	question-answering	2022-04-28T06:16:38Z	--- tags: - generated_from_trainer datasets: - squad model-index: - name: electra-small-discriminator-finetuned-squad-finetuned-squad results: [] --- <!-- This model card has been generated automatically according to the information the Trainer had access to. You should probably proofread and complete it, then remove this comment. --> # electra-small-discriminator-finetuned-squad-finetuned-squad This model is a fine-tuned version of [bdickson/electra-small-discriminator-finetuned-squad](https://huggingface.co/bdickson/electra-small-discriminator-finetuned-squad) on the squad dataset. ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 2e-05 - train_batch_size: 16 - eval_batch_size: 16 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - num_epochs: 5 ### Framework versions - Transformers 4.18.0 - Pytorch 1.11.0+cu113 - Datasets 2.1.0 - Tokenizers 0.12.1
Das282000Prit/fyp-finetuned-imdb	Das282000Prit	2022-04-28T05:53:55Z	4	0	transformers	[ "transformers", "tf", "distilbert", "fill-mask", "generated_from_keras_callback", "license:apache-2.0", "autotrain_compatible", "endpoints_compatible", "region:us" ]	fill-mask	2022-04-28T05:46:39Z	--- license: apache-2.0 tags: - generated_from_keras_callback model-index: - name: Das282000Prit/fyp-finetuned-imdb results: [] --- <!-- This model card has been generated automatically according to the information Keras had access to. You should probably proofread and complete it, then remove this comment. --> # Das282000Prit/fyp-finetuned-imdb This model is a fine-tuned version of [distilbert-base-uncased](https://huggingface.co/distilbert-base-uncased) on an unknown dataset. It achieves the following results on the evaluation set: - Train Loss: 2.8566 - Validation Loss: 2.6019 - Epoch: 0 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - optimizer: {'name': 'AdamWeightDecay', 'learning_rate': {'class_name': 'WarmUp', 'config': {'initial_learning_rate': 2e-05, 'decay_schedule_fn': {'class_name': 'PolynomialDecay', 'config': {'initial_learning_rate': 2e-05, 'decay_steps': -688, 'end_learning_rate': 0.0, 'power': 1.0, 'cycle': False, 'name': None}, '__passive_serialization__': True}, 'warmup_steps': 1000, 'power': 1.0, 'name': None}}, 'decay': 0.0, 'beta_1': 0.9, 'beta_2': 0.999, 'epsilon': 1e-08, 'amsgrad': False, 'weight_decay_rate': 0.01} - training_precision: mixed_float16 ### Training results \| Train Loss \| Validation Loss \| Epoch \| \|:----------:\|:---------------:\|:-----:\| \| 2.8566 \| 2.6019 \| 0 \| ### Framework versions - Transformers 4.18.0 - TensorFlow 2.8.0 - Datasets 2.1.0 - Tokenizers 0.12.1
snunlp/KR-FinBert	snunlp	2022-04-28T05:06:40Z	263	2	transformers	[ "transformers", "pytorch", "bert", "fill-mask", "ko", "autotrain_compatible", "endpoints_compatible", "region:us" ]	fill-mask	2022-03-02T23:29:05Z	--- language: - ko --- # KR-FinBert & KR-FinBert-SC Much progress has been made in the NLP (Natural Language Processing) field, with numerous studies showing that domain adaptation using small-scale corpus and fine-tuning with labeled data is effective for overall performance improvement. we proposed KR-FinBert for the financial domain by further pre-training it on a financial corpus and fine-tuning it for sentiment analysis. As many studies have shown, the performance improvement through adaptation and conducting the downstream task was also clear in this experiment. ![KR-FinBert](https://huggingface.co/snunlp/KR-FinBert/resolve/main/images/KR-FinBert.png) ## Data The training data for this model is expanded from those of [KR-BERT-MEDIUM](https://huggingface.co/snunlp/KR-Medium), texts from Korean Wikipedia, general news articles, legal texts crawled from the National Law Information Center and [Korean Comments dataset](https://www.kaggle.com/junbumlee/kcbert-pretraining-corpus-korean-news-comments). For the transfer learning, corporate related economic news articles from 72 media sources such as the Financial Times, The Korean Economy Daily, etc and analyst reports from 16 securities companies such as Kiwoom Securities, Samsung Securities, etc are added. Included in the dataset is 440,067 news titles with their content and 11,237 analyst reports. The total data size is about 13.22GB. For mlm training, we split the data line by line and the total no. of lines is 6,379,315. KR-FinBert is trained for 5.5M steps with the maxlen of 512, training batch size of 32, and learning rate of 5e-5, taking 67.48 hours to train the model using NVIDIA TITAN XP. ## Citation ``` @misc{kr-FinBert, author = {Kim, Eunhee and Hyopil Shin}, title = {KR-FinBert: KR-BERT-Medium Adapted With Financial Domain Data}, year = {2022}, publisher = {GitHub}, journal = {GitHub repository}, howpublished = {\url{https://huggingface.co/snunlp/KR-FinBert}} } ```
chv5/t5-small-shuffled_take1	chv5	2022-04-28T03:36:55Z	3	0	transformers	[ "transformers", "pytorch", "tensorboard", "t5", "text2text-generation", "generated_from_trainer", "dataset:xsum", "license:apache-2.0", "model-index", "autotrain_compatible", "text-generation-inference", "endpoints_compatible", "region:us" ]	text2text-generation	2022-04-27T20:27:04Z	--- license: apache-2.0 tags: - generated_from_trainer datasets: - xsum metrics: - rouge model-index: - name: t5-small-shuffled_take1 results: - task: name: Sequence-to-sequence Language Modeling type: text2text-generation dataset: name: xsum type: xsum args: default metrics: - name: Rouge1 type: rouge value: 11.9641 --- <!-- This model card has been generated automatically according to the information the Trainer had access to. You should probably proofread and complete it, then remove this comment. --> # t5-small-shuffled_take1 This model is a fine-tuned version of [t5-small](https://huggingface.co/t5-small) on the xsum dataset. It achieves the following results on the evaluation set: - Loss: 0.1788 - Rouge1: 11.9641 - Rouge2: 10.5245 - Rougel: 11.5825 - Rougelsum: 11.842 - Gen Len: 18.9838 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 2e-05 - train_batch_size: 6 - eval_batch_size: 6 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - num_epochs: 1 - mixed_precision_training: Native AMP ### Training results \| Training Loss \| Epoch \| Step \| Validation Loss \| Rouge1 \| Rouge2 \| Rougel \| Rougelsum \| Gen Len \| \|:-------------:\|:-----:\|:-----:\|:---------------:\|:-------:\|:-------:\|:-------:\|:---------:\|:-------:\| \| 0.2238 \| 1.0 \| 34008 \| 0.1788 \| 11.9641 \| 10.5245 \| 11.5825 \| 11.842 \| 18.9838 \| ### Framework versions - Transformers 4.18.0 - Pytorch 1.11.0+cu113 - Datasets 2.1.0 - Tokenizers 0.12.1
caush/Clickbait5	caush	2022-04-28T03:15:08Z	4	0	transformers	[ "transformers", "pytorch", "bert", "text-classification", "generated_from_trainer", "autotrain_compatible", "endpoints_compatible", "region:us" ]	text-classification	2022-04-28T02:50:04Z	--- tags: - generated_from_trainer model-index: - name: Clickbait5 results: [] --- <!-- This model card has been generated automatically according to the information the Trainer had access to. You should probably proofread and complete it, then remove this comment. --> # Clickbait5 This model was trained from scratch on the None dataset. It achieves the following results on the evaluation set: - Loss: 0.0258 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 5e-05 - train_batch_size: 16 - eval_batch_size: 16 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - num_epochs: 4 ### Training results \| Training Loss \| Epoch \| Step \| Validation Loss \| \|:-------------:\|:-----:\|:----:\|:---------------:\| \| No log \| 0.04 \| 50 \| 0.0258 \| \| No log \| 0.08 \| 100 \| 0.0269 \| \| No log \| 0.12 \| 150 \| 0.0259 \| \| No log \| 0.16 \| 200 \| 0.0260 \| \| No log \| 0.21 \| 250 \| 0.0267 \| \| No log \| 0.25 \| 300 \| 0.0276 \| \| No log \| 0.29 \| 350 \| 0.0284 \| \| No log \| 0.33 \| 400 \| 0.0270 \| \| No log \| 0.37 \| 450 \| 0.0269 \| \| 0.0195 \| 0.41 \| 500 \| 0.0260 \| \| 0.0195 \| 0.45 \| 550 \| 0.0284 \| ### Framework versions - Transformers 4.17.0 - Pytorch 1.11.0 - Datasets 2.0.0 - Tokenizers 0.11.6
Ahmed9275/ALL-2	Ahmed9275	2022-04-28T02:07:25Z	64	1	transformers	[ "transformers", "pytorch", "tensorboard", "vit", "image-classification", "huggingpics", "model-index", "autotrain_compatible", "endpoints_compatible", "region:us" ]	image-classification	2022-04-28T02:07:14Z	--- tags: - image-classification - pytorch - huggingpics metrics: - accuracy model-index: - name: ALL-2 results: - task: name: Image Classification type: image-classification metrics: - name: Accuracy type: accuracy value: 0.9855383038520813 --- # ALL-2 Autogenerated by HuggingPics🤗🖼️ Create your own image classifier for anything by running [the demo on Google Colab](https://colab.research.google.com/github/nateraw/huggingpics/blob/main/HuggingPics.ipynb). Report any issues with the demo at the [github repo](https://github.com/nateraw/huggingpics). ## Example Images
caush/Clickbait3	caush	2022-04-28T02:06:02Z	4	0	transformers	[ "transformers", "pytorch", "bert", "text-classification", "generated_from_trainer", "license:mit", "autotrain_compatible", "endpoints_compatible", "region:us" ]	text-classification	2022-04-28T01:53:58Z	--- license: mit tags: - generated_from_trainer model-index: - name: Clickbait3 results: [] --- <!-- This model card has been generated automatically according to the information the Trainer had access to. You should probably proofread and complete it, then remove this comment. --> # Clickbait3 This model is a fine-tuned version of [microsoft/Multilingual-MiniLM-L12-H384](https://huggingface.co/microsoft/Multilingual-MiniLM-L12-H384) on the None dataset. It achieves the following results on the evaluation set: - Loss: 0.0248 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 5e-05 - train_batch_size: 16 - eval_batch_size: 16 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - num_epochs: 4 ### Training results \| Training Loss \| Epoch \| Step \| Validation Loss \| \|:-------------:\|:-----:\|:----:\|:---------------:\| \| No log \| 0.05 \| 50 \| 0.0373 \| \| No log \| 0.1 \| 100 \| 0.0320 \| \| No log \| 0.15 \| 150 \| 0.0295 \| \| No log \| 0.21 \| 200 \| 0.0302 \| \| No log \| 0.26 \| 250 \| 0.0331 \| \| No log \| 0.31 \| 300 \| 0.0280 \| \| No log \| 0.36 \| 350 \| 0.0277 \| \| No log \| 0.41 \| 400 \| 0.0316 \| \| No log \| 0.46 \| 450 \| 0.0277 \| \| 0.0343 \| 0.51 \| 500 \| 0.0276 \| \| 0.0343 \| 0.56 \| 550 \| 0.0282 \| \| 0.0343 \| 0.62 \| 600 \| 0.0280 \| \| 0.0343 \| 0.67 \| 650 \| 0.0271 \| \| 0.0343 \| 0.72 \| 700 \| 0.0264 \| \| 0.0343 \| 0.77 \| 750 \| 0.0265 \| \| 0.0343 \| 0.82 \| 800 \| 0.0260 \| \| 0.0343 \| 0.87 \| 850 \| 0.0263 \| \| 0.0343 \| 0.92 \| 900 \| 0.0259 \| \| 0.0343 \| 0.97 \| 950 \| 0.0277 \| \| 0.0278 \| 1.03 \| 1000 \| 0.0281 \| \| 0.0278 \| 1.08 \| 1050 \| 0.0294 \| \| 0.0278 \| 1.13 \| 1100 \| 0.0256 \| \| 0.0278 \| 1.18 \| 1150 \| 0.0258 \| \| 0.0278 \| 1.23 \| 1200 \| 0.0254 \| \| 0.0278 \| 1.28 \| 1250 \| 0.0265 \| \| 0.0278 \| 1.33 \| 1300 \| 0.0252 \| \| 0.0278 \| 1.38 \| 1350 \| 0.0251 \| \| 0.0278 \| 1.44 \| 1400 \| 0.0264 \| \| 0.0278 \| 1.49 \| 1450 \| 0.0262 \| \| 0.023 \| 1.54 \| 1500 \| 0.0272 \| \| 0.023 \| 1.59 \| 1550 \| 0.0278 \| \| 0.023 \| 1.64 \| 1600 \| 0.0255 \| \| 0.023 \| 1.69 \| 1650 \| 0.0258 \| \| 0.023 \| 1.74 \| 1700 \| 0.0262 \| \| 0.023 \| 1.79 \| 1750 \| 0.0250 \| \| 0.023 \| 1.85 \| 1800 \| 0.0253 \| \| 0.023 \| 1.9 \| 1850 \| 0.0271 \| \| 0.023 \| 1.95 \| 1900 \| 0.0248 \| \| 0.023 \| 2.0 \| 1950 \| 0.0258 \| \| 0.0224 \| 2.05 \| 2000 \| 0.0252 \| \| 0.0224 \| 2.1 \| 2050 \| 0.0259 \| \| 0.0224 \| 2.15 \| 2100 \| 0.0254 \| \| 0.0224 \| 2.21 \| 2150 \| 0.0260 \| \| 0.0224 \| 2.26 \| 2200 \| 0.0254 \| \| 0.0224 \| 2.31 \| 2250 \| 0.0266 \| \| 0.0224 \| 2.36 \| 2300 \| 0.0258 \| \| 0.0224 \| 2.41 \| 2350 \| 0.0258 \| \| 0.0224 \| 2.46 \| 2400 \| 0.0256 \| ### Framework versions - Transformers 4.17.0 - Pytorch 1.11.0 - Datasets 2.0.0 - Tokenizers 0.11.6
yihsuan/best_model_0427_small_long	yihsuan	2022-04-28T01:51:38Z	15	0	transformers	[ "transformers", "pytorch", "tensorboard", "mt5", "text2text-generation", "summarization", "mT5", "zh", "autotrain_compatible", "endpoints_compatible", "region:us" ]	summarization	2022-04-27T09:08:17Z	--- tags: - summarization - mT5 language: - zh widget: - text: "專家稱維康桑格研究所(Wellcome Sanger Institute)的上述研究發現「令人震驚」而且「發人深省」。基因變異指關於我們身體成長和管理的相關指令，也就是DNA當中發生的變化。長期以來，變異一直被當作癌症的根源，但是數十年來關於變異是否對衰老有重要影響一直存在爭論。桑格研究所的研究人員說他們得到了「第一個試驗性證據」，證明了兩者的關係。他們分析了預期壽命各異的物種基因變異的不同速度。研究人員分析了貓、黑白疣猴、狗、雪貂、長頸鹿、馬、人、獅子、裸鼴鼠、兔子、老鼠、環尾狐猴和老虎等十幾種動物的DNA。發表在《自然》雜誌上的研究顯示，老鼠在短暫的生命當中每年經歷了將近800次變異，老鼠的壽命一般不到４年。" inference: parameters: max_length: 120 ---
yihsuan/best_model_0426_base	yihsuan	2022-04-28T01:44:27Z	15	1	transformers	[ "transformers", "pytorch", "tensorboard", "mt5", "text2text-generation", "summarization", "mT5", "zh", "autotrain_compatible", "endpoints_compatible", "region:us" ]	summarization	2022-04-26T09:05:10Z	--- tags: - summarization - mT5 language: - zh widget: - text: "專家稱維康桑格研究所(Wellcome Sanger Institute)的上述研究發現「令人震驚」而且「發人深省」。基因變異指關於我們身體成長和管理的相關指令，也就是DNA當中發生的變化。長期以來，變異一直被當作癌症的根源，但是數十年來關於變異是否對衰老有重要影響一直存在爭論。桑格研究所的研究人員說他們得到了「第一個試驗性證據」，證明了兩者的關係。他們分析了預期壽命各異的物種基因變異的不同速度。研究人員分析了貓、黑白疣猴、狗、雪貂、長頸鹿、馬、人、獅子、裸鼴鼠、兔子、老鼠、環尾狐猴和老虎等十幾種動物的DNA。發表在《自然》雜誌上的研究顯示，老鼠在短暫的生命當中每年經歷了將近800次變異，老鼠的壽命一般不到４年。" inference: parameters: max_length: 50 ---
Ahmed9275/ALL	Ahmed9275	2022-04-28T01:01:23Z	62	0	transformers	[ "transformers", "pytorch", "tensorboard", "vit", "image-classification", "huggingpics", "model-index", "autotrain_compatible", "endpoints_compatible", "region:us" ]	image-classification	2022-04-28T01:00:00Z	--- tags: - image-classification - pytorch - huggingpics metrics: - accuracy model-index: - name: ALL results: - task: name: Image Classification type: image-classification metrics: - name: Accuracy type: accuracy value: 0.9262039065361023 --- # ALL Autogenerated by HuggingPics🤗🖼️ Create your own image classifier for anything by running [the demo on Google Colab](https://colab.research.google.com/github/nateraw/huggingpics/blob/main/HuggingPics.ipynb). Report any issues with the demo at the [github repo](https://github.com/nateraw/huggingpics). ## Example Images
SerdarHelli/Brain-MRI-GAN	SerdarHelli	2022-04-27T20:32:07Z	0	0	null	[ "brainMRI", "GAN", "medicalimaging", "pytorch", "region:us" ]	null	2022-04-27T19:07:39Z	--- tags: - brainMRI - GAN - medicalimaging - pytorch metrics: - fid50k --- The model's kernels etc. source code ==> https://github.com/NVlabs/stylegan3
gagan3012/ArOCRv4	gagan3012	2022-04-27T20:23:52Z	4	0	transformers	[ "transformers", "pytorch", "tensorboard", "vision-encoder-decoder", "image-text-to-text", "generated_from_trainer", "doi:10.57967/hf/0018", "endpoints_compatible", "region:us" ]	image-text-to-text	2022-04-27T18:49:46Z	--- tags: - generated_from_trainer model-index: - name: ArOCRv4 results: [] --- <!-- This model card has been generated automatically according to the information the Trainer had access to. You should probably proofread and complete it, then remove this comment. --> # ArOCRv4 This model is a fine-tuned version of [](https://huggingface.co/) on an unknown dataset. It achieves the following results on the evaluation set: - Loss: 1.5811 - Cer: 0.1249 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 5e-05 - train_batch_size: 16 - eval_batch_size: 16 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - num_epochs: 5 - mixed_precision_training: Native AMP ### Training results \| Training Loss \| Epoch \| Step \| Validation Loss \| Cer \| \|:-------------:\|:-----:\|:----:\|:---------------:\|:-------:\| \| 3.103 \| 1.18 \| 1000 \| 8.0852 \| 11.5974 \| \| 1.2535 \| 2.36 \| 2000 \| 2.0400 \| 0.4904 \| \| 0.5682 \| 3.55 \| 3000 \| 1.9336 \| 0.2145 \| \| 0.3038 \| 4.73 \| 4000 \| 1.5811 \| 0.1249 \| ### Framework versions - Transformers 4.18.0 - Pytorch 1.9.1 - Datasets 2.1.0 - Tokenizers 0.11.6
iamholmes/english-phrases-bible	iamholmes	2022-04-27T19:48:58Z	69	0	sentence-transformers	[ "sentence-transformers", "pytorch", "distilbert", "feature-extraction", "sentence-similarity", "transformers", "license:apache-2.0", "autotrain_compatible", "text-embeddings-inference", "endpoints_compatible", "region:us" ]	sentence-similarity	2022-04-27T19:48:50Z	--- pipeline_tag: sentence-similarity license: apache-2.0 tags: - sentence-transformers - feature-extraction - sentence-similarity - transformers --- # sentence-transformers/msmarco-distilbert-base-tas-b This is a port of the [DistilBert TAS-B Model](https://huggingface.co/sebastian-hofstaetter/distilbert-dot-tas_b-b256-msmarco) to [sentence-transformers](https://www.SBERT.net) model: It maps sentences & paragraphs to a 768 dimensional dense vector space and is optimized for the task of semantic search. ## Usage (Sentence-Transformers) Using this model becomes easy when you have [sentence-transformers](https://www.SBERT.net) installed: ``` pip install -U sentence-transformers ``` Then you can use the model like this: ```python from sentence_transformers import SentenceTransformer, util query = "How many people live in London?" docs = ["Around 9 Million people live in London", "London is known for its financial district"] #Load the model model = SentenceTransformer('sentence-transformers/msmarco-distilbert-base-tas-b') #Encode query and documents query_emb = model.encode(query) doc_emb = model.encode(docs) #Compute dot score between query and all document embeddings scores = util.dot_score(query_emb, doc_emb)[0].cpu().tolist() #Combine docs & scores doc_score_pairs = list(zip(docs, scores)) #Sort by decreasing score doc_score_pairs = sorted(doc_score_pairs, key=lambda x: x[1], reverse=True) #Output passages & scores for doc, score in doc_score_pairs: print(score, doc) ``` ## Usage (HuggingFace Transformers) Without [sentence-transformers](https://www.SBERT.net), you can use the model like this: First, you pass your input through the transformer model, then you have to apply the right pooling-operation on-top of the contextualized word embeddings. ```python from transformers import AutoTokenizer, AutoModel import torch #CLS Pooling - Take output from first token def cls_pooling(model_output): return model_output.last_hidden_state[:,0] #Encode text def encode(texts): # Tokenize sentences encoded_input = tokenizer(texts, padding=True, truncation=True, return_tensors='pt') # Compute token embeddings with torch.no_grad(): model_output = model(*encoded_input, return_dict=True) # Perform pooling embeddings = cls_pooling(model_output) return embeddings # Sentences we want sentence embeddings for query = "How many people live in London?" docs = ["Around 9 Million people live in London", "London is known for its financial district"] # Load model from HuggingFace Hub tokenizer = AutoTokenizer.from_pretrained("sentence-transformers/msmarco-distilbert-base-tas-b") model = AutoModel.from_pretrained("sentence-transformers/msmarco-distilbert-base-tas-b") #Encode query and docs query_emb = encode(query) doc_emb = encode(docs) #Compute dot score between query and all document embeddings scores = torch.mm(query_emb, doc_emb.transpose(0, 1))[0].cpu().tolist() #Combine docs & scores doc_score_pairs = list(zip(docs, scores)) #Sort by decreasing score doc_score_pairs = sorted(doc_score_pairs, key=lambda x: x[1], reverse=True) #Output passages & scores for doc, score in doc_score_pairs: print(score, doc) ``` ## Evaluation Results For an automated evaluation of this model, see the Sentence Embeddings Benchmark*: [https://seb.sbert.net](https://seb.sbert.net?model_name=sentence-transformers/msmarco-distilbert-base-tas-b) ## Full Model Architecture ``` SentenceTransformer( (0): Transformer({'max_seq_length': 512, 'do_lower_case': False}) with Transformer model: DistilBertModel (1): Pooling({'word_embedding_dimension': 768, 'pooling_mode_cls_token': True, 'pooling_mode_mean_tokens': False, 'pooling_mode_max_tokens': False, 'pooling_mode_mean_sqrt_len_tokens': False}) ) ``` ## Citing & Authors Have a look at: [DistilBert TAS-B Model](https://huggingface.co/sebastian-hofstaetter/distilbert-dot-tas_b-b256-msmarco)
princeton-nlp/efficient_mlm_m0.15-801010	princeton-nlp	2022-04-27T18:54:45Z	3	0	transformers	[ "transformers", "pytorch", "roberta", "fill-mask", "arxiv:2202.08005", "autotrain_compatible", "region:us" ]	fill-mask	2022-04-22T18:45:04Z	--- inference: false --- This is a model checkpoint for ["Should You Mask 15% in Masked Language Modeling"](https://arxiv.org/abs/2202.08005) [(code)](https://github.com/princeton-nlp/DinkyTrain.git). We use pre layer norm, which is not supported by HuggingFace. To use our model, go to our [github repo](https://github.com/princeton-nlp/DinkyTrain.git), download our code, and import the RoBERTa class from `huggingface/modeling_roberta_prelayernorm.py`. For example, ``` bash from huggingface.modeling_roberta_prelayernorm import RobertaForMaskedLM, RobertaForSequenceClassification ```
princeton-nlp/efficient_mlm_m0.40	princeton-nlp	2022-04-27T18:54:13Z	5	0	transformers	[ "transformers", "pytorch", "roberta", "fill-mask", "arxiv:2202.08005", "autotrain_compatible", "region:us" ]	fill-mask	2022-04-22T18:44:55Z	--- inference: false --- This is a model checkpoint for ["Should You Mask 15% in Masked Language Modeling"](https://arxiv.org/abs/2202.08005) [(code)](https://github.com/princeton-nlp/DinkyTrain.git). We use pre layer norm, which is not supported by HuggingFace. To use our model, go to our [github repo](https://github.com/princeton-nlp/DinkyTrain.git), download our code, and import the RoBERTa class from `huggingface/modeling_roberta_prelayernorm.py`. For example, ``` bash from huggingface.modeling_roberta_prelayernorm import RobertaForMaskedLM, RobertaForSequenceClassification ```
LiYuan/amazon-cross-encoder	LiYuan	2022-04-27T18:36:36Z	5	1	transformers	[ "transformers", "pytorch", "roberta", "text-classification", "generated_from_trainer", "license:apache-2.0", "autotrain_compatible", "endpoints_compatible", "region:us" ]	text-classification	2022-04-27T18:06:28Z	--- license: apache-2.0 tags: - generated_from_trainer metrics: - accuracy model-index: - name: distilbert-base-uncased-finetuned-mnli results: [] --- <!-- This model card has been generated automatically according to the information the Trainer had access to. You should probably proofread and complete it, then remove this comment. --> # distilbert-base-uncased-finetuned-mnli This model is a fine-tuned version of [distilbert-base-uncased](https://huggingface.co/distilbert-base-uncased) on an unknown dataset. It achieves the following results on the evaluation set: - Loss: 0.8244 - Accuracy: 0.6617 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 2e-05 - train_batch_size: 16 - eval_batch_size: 16 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - num_epochs: 2 ### Training results \| Training Loss \| Epoch \| Step \| Validation Loss \| Accuracy \| \|:-------------:\|:-----:\|:-----:\|:---------------:\|:--------:\| \| 0.8981 \| 1.0 \| 35702 \| 0.8662 \| 0.6371 \| \| 0.7837 \| 2.0 \| 71404 \| 0.8244 \| 0.6617 \| ### Framework versions - Transformers 4.18.0 - Pytorch 1.11.0+cu113 - Datasets 2.1.0 - Tokenizers 0.12.1
HannahRoseKirk/Hatemoji	HannahRoseKirk	2022-04-27T18:17:04Z	30	4	transformers	[ "transformers", "pytorch", "deberta", "text-classification", "hate-speech-detection", "en", "dataset:HatemojiBuild", "dataset:HatemojiCheck", "arxiv:2108.05921", "arxiv:2012.15761", "arxiv:2202.11176", "license:cc-by-4.0", "autotrain_compatible", "endpoints_compatible", "region:us" ]	text-classification	2022-04-13T09:36:34Z	--- license: cc-by-4.0 language: - en tags: - text-classification - pytorch - hate-speech-detection datasets: - HatemojiBuild - HatemojiCheck metrics: - Accuracy, F1 Score --- # Hatemoji Model ## Model description This model is a fine-tuned version of the [DeBERTa base model](https://huggingface.co/microsoft/deberta-base). This model is cased. The model was trained on iterative rounds of adversarial data generation with human-and-model-in-the-loop. In each round, annotators are tasked with tricking the model-in-the-loop with emoji-containing statements that it will misclassify. Between each round, the model is retrained. This is the final model from the iterative process, referred to as R8-T in our paper. The intended task is to classify an emoji-containing statement as either non-hateful (LABEL 0.0) or hateful (LABEL 1.0). - Github Repository: https://github.com/HannahKirk/Hatemoji - HuggingFace Datasets: [HatemojiBuild](https://huggingface.co/datasets/HannahRoseKirk/HatemojiBuild) & [HatemojiCheck](https://huggingface.co/datasets/HannahRoseKirk/HatemojiCheck) - Paper: https://arxiv.org/abs/2108.05921 - Point of Contact: [email protected] ## Intended uses & limitations The intended use of the model is to classify English-language, emoji-containing, short-form text documents as a binary task: non-hateful vs hateful. The model has demonstrated strengths compared to commercial and academic models on classifying emoji-based hate, but is also a strong classifier of text-only hate. Because the model was trained on synthetic, adversarially-generated data, it may have some weaknesses when it comes to empirical emoji-based hate 'in-the-wild'. You can interact with this model on [Dynabench](https://dynabench.org/tasks/hs), and find its limitations. We hope to continue improving the model on new adversarial data to better iron out its remaining weaknesses! ## How to use The model can be used with pipeline: ```python from transformers import pipeline classifier = pipeline("text-classification",model='HannahRoseKirk/Hatemoji', return_all_scores=True) prediction = classifier("I 💜💙💚 emoji 😍", ) print(prediction) """ Output [[{'label': 'LABEL_0', 'score': 0.9999157190322876}, {'label': 'LABEL_1', 'score': 8.425049600191414e-05}]] """ ``` ### Training data The model was trained on: * The three rounds of emoji-containing, adversarially-generated texts from [HatemojiBuild](https://huggingface.co/datasets/HannahRoseKirk/HatemojiBuild) * The four rounds of text-only, adversarially-generated texts from Vidgen et al., (2021). _Learning from the worst: Dynamically generated datasets to improve online hate detection_. Available on [Github](https://github.com/bvidgen/Dynamically-Generated-Hate-Speech-Dataset) and explained in their [paper](https://arxiv.org/abs/2012.15761). * A collection of widely available and publicly accessible datasets from [https://hatespeechdata.com/](hatespeechdata.com) ## Train procedure The model was trained using HuggingFace's [run glue script](https://github.com/huggingface/transformers/blob/main/examples/pytorch/text-classification/run_glue.py), using the following parameters: ``` python3 transformers/examples/pytorch/text-classification/run_glue.py \ --model_name_or_path microsoft/deberta-base \ --validation_file path_to_data/dev.csv \ --train_file path_to_data/train.csv \ --do_train --do_eval --max_seq_length 512 --learning_rate 2e-5 \ --num_train_epochs 3 --evaluation_strategy epoch \ --load_best_model_at_end --output_dir path_to_outdir/deberta123/ \ --seed 123 \ --cache_dir /.cache/huggingface/transformers/ \ --overwrite_output_dir > ./log_deb 2> ./err_deb ``` We experimented with upsampling the train split of each round to improve performance with increments of [1, 5, 10, 100], with the optimum upsampling taken forward to all subsequent rounds. The optimal upsampling ratios for R1-R4 (text rounds from Vidgen et al.,) are carried forward. This model is trained on upsampling ratios of `{'R0':1, 'R1':5, 'R2':100, 'R3':1, 'R4':1 , 'R5':100, 'R6':1, 'R7':5}`. ## Variable and metrics We wished to train a model which could effectively encode information about emoji-based hate, without worsening performance on text-only hate. Thus, we evaluate the model on: * [HatemojiCheck](https://huggingface.co/datasets/HannahRoseKirk/HatemojiCheck), an evaluation checklist with 7 functionalities of emoji-based hate and contrast sets * [HateCheck](https://huggingface.co/datasets/Paul/hatecheck), an evaluation checklist contains 29 functional tests for hate speech and contrast sets. * The held-out tests sets from [HatemojiBuild](https://huggingface.co/datasets/HannahRoseKirk/HatemojiBuild) the three round of adversarially-generated data collection with emoji-containing examples (R5-7). Available on Huuggingface * The held-out test sets from the four rounds of adversarially-generated data collection with text-only examples (R1-4, from [Vidgen et al.](https://github.com/bvidgen/Dynamically-Generated-Hate-Speech-Dataset)) For the round-specific test sets, we used a weighted F1-score across them to choose the final model in each round. For more details, see our [paper](https://arxiv.org/abs/2108.05921) ## Evaluation results We compare our model to: * P-IA: the identity attack attribute from Perspective API * P-TX: the toxicity attribute from Perspective API * B-D: A BERT model trained on the [Davidson et al. (2017)](https://github.com/t-davidson/hate-speech-and-offensive-language) dataset * B-F: A BERT model trained on the [Founta et al. (2018)](https://github.com/ENCASEH2020/hatespeech-twitter) dataset \| \| Emoji Test Sets \| \| \| \| Text Test Sets \| \| \| \| All Rounds \| \| \| :------- \| :-----------------: \| :--------: \| :------------: \| :--------: \| :----------------: \| :--------: \| :-----------: \| :--------: \| :------------: \| :--------: \| \| \| R5-R7 \| \| HmojiCheck \| \| R1-R4 \| \| HateCheck \| \| R1-R7 \| \| \| \| Acc \| F1 \| Acc \| F1 \| Acc \| F1 \| Acc \| F1 \| Acc \| F1 \| \| P-IA \| 0\.508 \| 0\.394 \| 0\.689 \| 0\.754 \| 0\.679 \| 0\.720 \| 0\.765 \| 0\.839 \| 0\.658 \| 0\.689 \| \| P-TX \| 0\.523 \| 0\.448 \| 0\.650 \| 0\.711 \| 0\.602 \| 0\.659 \| 0\.720 \| 0\.813 \| 0\.592 \| 0\.639 \| \| B-D \| 0\.489 \| 0\.270 \| 0\.578 \| 0\.636 \| 0\.589 \| 0\.607 \| 0\.632 \| 0\.738 \| 0\.591 \| 0\.586 \| \| B-F \| 0\.496 \| 0\.322 \| 0\.552 \| 0\.605 \| 0\.562 \| 0\.562 \| 0\.602 \| 0\.694 \| 0\.557 \| 0\.532 \| \| Hatemoji \| 0\.744 \| 0\.755 \| 0\.871 \| 0\.904 \| 0\.827 \| 0\.844 \| 0\.966 \| 0\.975 \| 0\.814 \| 0\.829 \| For full discussion of the model results, see our [paper](https://arxiv.org/abs/2108.05921). A recent [paper](https://arxiv.org/pdf/2202.11176.pdf) by Lees et al., (2022) _A New Generation of Perspective API:Efficient Multilingual Character-level Transformers_ beats this model on the HatemojiCheck benchmark.
obokkkk/mbart-large-cc25-finetuned-en-to-ko2	obokkkk	2022-04-27T17:49:20Z	5	0	transformers	[ "transformers", "pytorch", "tensorboard", "mbart", "text2text-generation", "generated_from_trainer", "autotrain_compatible", "endpoints_compatible", "region:us" ]	text2text-generation	2022-04-27T15:00:41Z	--- tags: - generated_from_trainer model-index: - name: mbart-large-cc25-finetuned-en-to-ko2 results: [] --- <!-- This model card has been generated automatically according to the information the Trainer had access to. You should probably proofread and complete it, then remove this comment. --> # mbart-large-cc25-finetuned-en-to-ko2 This model is a fine-tuned version of [facebook/mbart-large-cc25](https://huggingface.co/facebook/mbart-large-cc25) on the None dataset. ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 2e-05 - train_batch_size: 16 - eval_batch_size: 16 - seed: 42 - gradient_accumulation_steps: 128 - total_train_batch_size: 2048 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - num_epochs: 1 ### Framework versions - Transformers 4.18.0 - Pytorch 1.11.0+cu113 - Datasets 2.1.0 - Tokenizers 0.12.1

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